Python PSS/E Simulation to Test Efficacy of Proposed PMU Based WAMS and Potential WAMPAC Applications

A PSS/E 32 model of a real section of the Northern Ireland electrical grid was dynamically controlled with Python 2.5. In this manner data from a proposed wide area monitoring system was simulated. The area is of interest as it is a weakly coupled distribution grid with significant distributed generation. The data was used to create an optimization and protection metric that reflected reactive power flow, voltage profile, thermal overload and voltage excursions. Step changes in the metric were introduced upon the operation of special protection systems and voltage excursions. A wide variety of grid conditions were simulated while tap changer positions and switched capacitor banks were iterated through; with the most desirable state returning the lowest optimization and protection metric. The optimized metric was compared against the metric generated from the standard system state returned by PSS/E. Various grid scenarios were explored involving an intact network and compromised networks (line loss) under summer maximum, summer minimum and winter maximum conditions. In each instance the output from the installed distributed generation is varied between 0 MW and 80 MW (120% of installed capacity). It is shown that in grid models the triggering of special protection systems is delayed by between 1 MW and 6 MW (1.5% to 9% of capacity), with 3.5 MW being the average. The optimization and protection metric gives a quantitative value for system health and demonstrates the potential efficacy of wide area monitoring for protection and control.

Reference clock design for low power and low phase noise with temperature compensation

Thesis (Ph.D.)--University of Washington, 2016-03

Simulation model for driving dynamics, energy use and power supply

Dissertação para obtenção do grau de Mestre em Engenharia Electrotécnica Ramo de Energia

Modified Particle Swarm Optimization Applied to Integrated Demand Response and DG Resources Scheduling

The elastic behavior of the demand consumption jointly used with other available resources such as distributed generation (DG) can play a crucial role for the success of smart grids. The intensive use of Distributed Energy Resources (DER) and the technical and contractual constraints result in large-scale non linear optimization problems that require computational intelligence methods to be solved. This paper proposes a Particle Swarm Optimization (PSO) based methodology to support the minimization of the operation costs of a virtual power player that manages the resources in a distribution network and the network itself. Resources include the DER available in the considered time period and the energy that can be bought from external energy suppliers. Network constraints are considered. The proposed approach uses Gaussian mutation of the strategic parameters and contextual self-parameterization of the maximum and minimum particle velocities. The case study considers a real 937 bus distribution network, with 20310 consumers and 548 distributed generators. The obtained solutions are compared with a deterministic approach and with PSO without mutation and Evolutionary PSO, both using self-parameterization.

Retificador trifásico controlado com fator de potência quase unitário

Dissertação de Mestrado para obtenção do grau de Mestre em Engenharia Eletrotécnica Ramo Automação e Eletrónica Industrial

Associations of short-term particle and noise exposures with markers of cardiovascular and respiratory health among highway maintenance workers

BACKGROUND: Highway maintenance workers are constantly and simultaneously exposed to traffic-related particle and noise emissions, and both have been linked to increased cardiovascular morbidity and mortality in population-based epidemiology studies. OBJECTIVES: We aimed to investigate short-term health effects related to particle and noise exposure. METHODS: We monitored 18 maintenance workers, during as many as five 24-hour periods from a total of 50 observation days. We measured their exposure to fine particulate matter (PM2.5), ultrafine particles, noise, and the cardiopulmonary health endpoints: blood pressure, pro-inflammatory and pro-thrombotic markers in the blood, lung function and fractional exhaled nitric oxide (FeNO) measured approximately 15 hours post-work. Heart rate variability was assessed during a sleep period approximately 10 hours post-work. RESULTS: PM2.5 exposure was significantly associated with C-reactive protein and serum amyloid A, and negatively associated with tumor necrosis factor α. None of the particle metrics were significantly associated with von Willebrand factor or tissue factor expression. PM2.5 and work noise were associated with markers of increased heart rate variability, and with increased HF and LF power. Systolic and diastolic blood pressure on the following morning were significantly associated with noise exposure after work, and non-significantly associated with PM2.5. We observed no significant associations between any of the exposures and lung function or FeNO. CONCLUSIONS: Our findings suggest that exposure to particles and noise during highway maintenance work might pose a cardiovascular health risk. Actions to reduce these exposures could lead to better health for this population of workers.

Mechanisms of endothelin-1 induced reactive oxygen species production in vascular adventitial fibroblasts

With the relationship between endothelin-1 (ET-1) stimulation and reactive oxygen species (ROS) production unknown in adventitial fibroblasts, I examined the ROS response to ET-1 and angiotensin (Ang II). ET-1 -induced ROS peaked following 4 hrs of ET-1 stimulation and was inhibited by an ETA receptor antagonist (BQ 123, 1 uM) an extracellular signal-regulated kinase (ERK) 1/2 inhibitor (PD98059, 10 uM), and by both a specific, apocynin (10 uM), and non-specific, diphenyleneiodonium (10 uM), NAD(P)H oxidase inhibitor. NOX2 knockout fibroblasts did not produce an ET-1 induced change in ROS levels. Ang II treatment increased ROS levels in a biphasic manner, with the second peak occurring 6 hrs following stimulation. The secondary phase of Ang II induced ROS was inhibited by an ATi receptor antagonist, Losartan (100 uM) and BQ 123. In conclusion, ET-1 induces ROS production primarily through an ETA-ERKl/2 NOX2 pathway, additionally, Ang II-induced ROS production also involves an ETa pathway.

Baroreflex sensitivity and developmental coordination disorder

Developmental coordination disorder (DCD) is a motor coordination disorder that is characterized by impairment of motor skills which leads to challenges with performing activities of daily living. Children with DCD have been shown to be less physically active and have increased body fatness. This is an important finding since a sedentary lifestyle and obesity are risk factors for cardiovascular disease. One indicator of cardiovascular health is baroreflex sensitivity (BRS), which is a measure of short term BP regulation that is accomplished through changes in HR. Diminished BRS is predictive of cardiovascular morbidity and mortality. The purpose of this study was to investigate BRS in 117 children aged 12 to 13 years with probable DCD (pOCO) and their matched controls with normal coordination. Following 15 minutes of supine rest, five minutes of continuous beat-by-beat blood pressure (Finapres) and RR interval were recorded (standard ECG). Spectral indices were computed using Fast Fourier Transform and transfer function analysis was used to compute BRS. High frequency and low frequency power spectral areas were set to 0.15-0.6 Hz and 0.04-0.15 Hz, respectively. BRS was compared between groups with an independent t-test and the difference was not significant. It is likely that a difference in BRS was not seen between groups since the difference in BMI between groups was small. As well, differences in BRS may not have manifested yet at this early age. However, the cardiovascular health of this population still deserves attention since differences in body composition and fitness were found between groups.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Subseasonal extremes of precipitation and active-break cycles of the Indian summer monsoon in a climate-change scenario

Changes to the behaviour of subseasonal precipitation extremes and active-break cycles of the Indian summer monsoon are assessed in this study using pre-industrial and 2 × CO2 integrations of the Hadley Centre coupled model HadCM3, which is able to simulate the monsoon seasonal cycle reasonably. At 2 × CO2, mean summer rainfall increases slightly, especially over central and northern India. The mean intensity of daily precipitation during the monsoon is found to increase, consistent with fewer wet days, and there are increases to heavy rain events beyond changes in the mean alone. The chance of reaching particular thresholds of heavy rainfall is found to approximately double over northern India, increasing the likelihood of damaging floods on a seasonal basis. The local distribution of such projections is uncertain, however, given the large spread in mean monsoon rainfall change and associated extremes amongst even the most recent coupled climate models. The measured increase of the heaviest precipitation events over India is found to be broadly in line with the degree of atmospheric warming and associated increases in specific humidity, lending a degree of predictability to changes in rainfall extremes. Active-break cycles of the Indian summer monsoon, important particularly due to their effect on agricultural output, are shown to be reasonably represented in HadCM3, in particular with some degree of northward propagation. We note an intensification of both active and break events, particularly when measured against the annual cycle, although there is no suggestion of any change to the duration or likelihood of monsoon breaks. Copyright © 2009 Royal Meteorological Society

Roman barrows and their landscape context: a GIS case study at Bartlow, Cambridgeshire

This paper examines the landscape context of the Bartlow Hills, a group of large Romano-British barrows that were excavated in the 1840s but have been largely neglected since. GIS is employed to test whether it was possible to view the mounds from nearby roads, barrows and villas. Existing research on provincial barrows, and especially their landscape context, and some recent relevant applications of GIS are reviewed. We argue that barrows are active and symbolically charged statements about power and identity. The most striking pattern to emerge from the GIS analysis is a focus on display to a local rather than a transient audience.

The impact of large scale atmospheric circulation patterns on wind power generation and its potential predictability: a case study over the UK

Over recent years there has been an increasing deployment of renewable energy generation technologies, particularly large-scale wind farms. As wind farm deployment increases, it is vital to gain a good understanding of how the energy produced is affected by climate variations, over a wide range of time-scales, from short (hours to weeks) to long (months to decades) periods. By relating wind speed at specific sites in the UK to a large-scale climate pattern (the North Atlantic Oscillation or "NAO"), the power generated by a modelled wind turbine under three different NAO states is calculated. It was found that the wind conditions under these NAO states may yield a difference in the mean wind power output of up to 10%. A simple model is used to demonstrate that forecasts of future NAO states can potentially be used to improve month-ahead statistical forecasts of monthly-mean wind power generation. The results confirm that the NAO has a significant impact on the hourly-, daily- and monthly-mean power output distributions from the turbine with important implications for (a) the use of meteorological data (e.g. their relationship to large scale climate patterns) in wind farm site assessment and, (b) the utilisation of seasonal-to-decadal climate forecasts to estimate future wind farm power output. This suggests that further research into the links between large-scale climate variability and wind power generation is both necessary and valuable.

Radicals from the atmospheric pressure pyrolysis and oxidative pyrolysis of hydroquinone, catechol, and phenol

The burning of tobacco creates various types of free radicals that have been reported to be biologically active. Some radicals are transient but can initiate catalytic cycles that generate other free radicals. Other radicals are environmentally persistent and can exist in total particulate matter (TPM) for extended periods. In spite of their importance, little is known concerning the precursors of these radicals or under what pyrolysis/combustion conditions they are formed. We performed studies of the formation of radicals from the gas-phase pyrolysis and oxidative pyrolysis of hydroquinone (HQ) and catechol (CT) between 750 and 1000 °C and phenol from 500 to 1000 °C. The initial electron paramagnetic resonance (EPR) spectra were complex, indicating the presence of multiple radicals. Using matrix annealing and microwave power saturation techniques, phenoxyl, cyclopentadienyl, and peroxyl radicals were identifiable, but only cyclopentadienyl radicals were stable above 750 °C.

Note: Active optical detection of cloud from a balloon platform

A disposable backscatter instrument is described for optical detection of cloud in the atmosphere from a balloon-carried platform. It uses an ultra-bright light emitting diode (LED) illumination source with a photodiode detector. Scattering of the LED light by cloud droplets generates a small optical signal which is separated from background light fluctuations using a lock-in technique. The signal to noise obtained permits cloud detection using the scattered LED light, even in daytime. The response is interpreted in terms of the equivalent visual range within the cloud. The device is lightweight (150 g) and low power (∼30 mA), for use alongside a conventional meteorological radiosonde.