Sediment budget monitoring of a debris-flow torrent (French Prealps)

The Manival near Grenoble (French Prealps) is a very active debris-flow torrent equipped with a large sediment trap (25 000 m3) protecting an urbanized alluvial fan from debris-flows. We began monitoring the sediment budget of the catchment controlled by the trap in Spring 2009. Terrestrial laser scanner is used for monitoring topographic changes in a small gully, the main channel, and the sediment trap. In the main channel, 39 cross-sections are surveyed after every event. Three periods of intense geomorphic activity are documented here. The first was induced by a convective storm in August 2009 which triggered a debris-flow that deposited ~1,800 m3 of sediment in the trap. The debris-flow originated in the upper reach of the main channel and our observations showed that sediment outputs were entirely supplied by channel scouring. Hillslope debris-flows were initiated on talus slopes, as revealed by terrestrial LiDAR resurveys; however they were disconnected to the main channel. The second and third periods of geomorphic activity were induced by long duration and low intensity rainfall events in September and October 2009 which generate small flow events with intense bedload transport. These events contribute to recharge the debris-flow channel with sediments by depositing important gravel dunes propagating from headwaters. The total recharge in the torrent subsequent to bedload transport events was estimated at 34% of the sediment erosion induced by the August debris-flow.

Moniensiöinen muuntaja taajuudenmuuttajakäytössä

Suuritehoisissa pumppu- ja puhallinkäytöissä käytetään usein suurnopeusmoottoria,jota syötetään välijännitteellä. Suurjännitetaajuudenmuuttajat ovat kalliita, eikä niitä ole aina edes mahdollista valmistaa. Tutkimuksen kohteena on rinnakkaisilla pienjännitetaajuudenmuuttajilla toteutettu sähkökäyttö, jossa pienjännite nostetaan moniensiöisellä muuntajalla suurjännitteeksi (6,6 kV) ja syötetään edelleen kuormana olevalle suurnopeusmoottorille. Opinnäytetyössä tutkitaan moniensiöisen muuntajan syöttöä rinnakkaisilla taajuudenmuuttajilla sekä niiden aiheuttamia häiriöitä toisilleen. Työssä tutkitaan myös harmonisten yliaaltojen vaikutuksia muuntajan häviöihin ja magnetointiominaisuuksiin. Taajuudenmuuttajan lähtöjännite ja -virta suodatetaan sinisuotimella, jonka parametreja simuloidaan Simulink- ohjelmistolla. Tavoite on löytää optimaaliset parametrit taajuudenmuuttajanlähtösuotimelle käyrämuotojen ja suotimeen jäävän tehon suhteen. Työssä tarkasteltiin sinisuodinta, johon jää 3 prosenttia syöttöjännitteestä. LC-suodin kompensoi sähkökäytön loistehon lähes kokonaan, joten taajuudenmuuttajien antotehon kannalta suotimet ovat perusteltuja. Taajuudenmuuttajan näennäisteho putoaa 22 prosenttia, joten taajuudenmuuttajat voidaan vastaavasti mitoittaa pienemmiksi.

Development of power flows around Baltic countries with new grid links

In the Thesis main focus is done on power flow development paths around the Baltic States as well as on market-based requirements for creation of the common Baltic electricity market. Current market regulations between the countries are presented; barriers for creating competitive common Baltic power market and for electricity trading with third countries are clarified; solutions are offered and corresponding road map is developed. Future power development paths around the Baltic States are analysed. For this purpose the 330 kV transmission grid of Estonia, Latvia and Lithuania is modelled in a power flow tool. Power flow calculations are carried out for winter and summer peak and off-peak load periods in 2020 with different combinations of interconnections. While carrying out power balance experiments several power flow patterns in the Baltic States are revealed. Conclusions are made about security of supply, grid congestion and transmission capacity availability for different scenarios.

LVDC power distribution system: computational modelling

In the doctoral dissertation, low-voltage direct current (LVDC) distribution system stability, supply security and power quality are evaluated by computational modelling and measurements on an LVDC research platform. Computational models for the LVDC network analysis are developed. Time-domain simulation models are implemented in the time-domain simulation environment PSCAD/EMTDC. The PSCAD/EMTDC models of the LVDC network are applied to the transient behaviour and power quality studies. The LVDC network power loss model is developed in a MATLAB environment and is capable of fast estimation of the network and component power losses. The model integrates analytical equations that describe the power loss mechanism of the network components with power flow calculations. For an LVDC network research platform, a monitoring and control software solution is developed. The solution is used to deliver measurement data for verification of the developed models and analysis of the modelling results. In the work, the power loss mechanism of the LVDC network components and its main dependencies are described. Energy loss distribution of the LVDC network components is presented. Power quality measurements and current spectra are provided and harmonic pollution on the DC network is analysed. The transient behaviour of the network is verified through time-domain simulations. DC capacitor guidelines for an LVDC power distribution network are introduced. The power loss analysis results show that one of the main optimisation targets for an LVDC power distribution network should be reduction of the no-load losses and efficiency improvement of converters at partial loads. Low-frequency spectra of the network voltages and currents are shown, and harmonic propagation is analysed. Power quality in the LVDC network point of common coupling (PCC) is discussed. Power quality standard requirements are shown to be met by the LVDC network. The network behaviour during transients is analysed by time-domain simulations. The network is shown to be transient stable during large-scale disturbances. Measurement results on the LVDC research platform proving this are presented in the work.

Modélisation statistique de l’érosion de cavitation d’une turbine hydraulique selon les paramètres d’opération

Dans une turbine hydraulique, la rotation des aubes dans l’eau crée une zone de basse pression, amenant l’eau à passer de l’état liquide à l’état gazeux. Ce phénomène de changement de phase est appelé cavitation et est similaire à l’ébullition. Lorsque les cavités de vapeur formées implosent près des parois, il en résulte une érosion sévère des matériaux, accélérant de façon importante la dégradation de la turbine. Un système de détection de l’érosion de cavitation à l’aide de mesures vibratoires, employable sur les turbines en opération, a donc été installé sur quatre groupes turbine-alternateur d’une centrale et permet d’estimer précisément le taux d’érosion en kg/ 10 000 h. Le présent projet vise à répondre à deux objectifs principaux. Premièrement, étudier le comportement de la cavitation sur un groupe turbine-alternateur cible et construire un modèle statistique, dans le but de prédire la variable cavitation en fonction des variables opératoires (tels l’ouverture de vannage, le débit, les niveaux amont et aval, etc.). Deuxièmement, élaborer une méthodologie permettant la reproductibilité de l’étude à d’autres sites. Une étude rétrospective sera effectuée et on se concentrera sur les données disponibles depuis la mise à jour du système en 2010. Des résultats préliminaires ont mis en évidence l’hétérogénéité du comportement de cavitation ainsi que des changements entre la relation entre la cavitation et diverses variables opératoires. Nous nous proposons de développer un modèle probabiliste adapté, en utilisant notamment le regroupement hiérarchique et des modèles de régression linéaire multiple.

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.

An Interpolative Analytical Cache Model with Application to Performance-Power Design Space Exploration

Caches are known to consume up to half of all system power in embedded processors. Co-optimizing performance and power of the cache subsystems is therefore an important step in the design of embedded systems, especially those employing application specific instruction processors. In this project, we propose an analytical cache model that succinctly captures the miss performance of an application over the entire cache parameter space. Unlike exhaustive trace driven simulation, our model requires that the program be simulated once so that a few key characteristics can be obtained. Using these application-dependent characteristics, the model can span the entire cache parameter space consisting of cache sizes, associativity and cache block sizes. In our unified model, we are able to cater for direct-mapped, set and fully associative instruction, data and unified caches. Validation against full trace-driven simulations shows that our model has a high degree of fidelity. Finally, we show how the model can be coupled with a power model for caches such that one can very quickly decide on pareto-optimal performance-power design points for rapid design space exploration.

DC traction load flow including AC distribution network

In the first part some information and characterisation about an AC distribution network that feeds traction substations and their possible influences on the DC traction load flow are presented. Those influences are investigated and mathematically modelled. To corroborate the mathematical model, an example is presented and their results are confronted with real measurements.

Um método primal-dual aplicado na resolução do problema de fluxo de potência ótimo

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Determinação da solução ótima do problema de fluxo de potência ótimo via análise de sensibilidade

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Um método eficiente para resolução do problema de Despacho Ótimo de Reativos com controle discreto do TAP dos transformadores

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

An efficient Hopfield network to solve economic dispatch problems with transmission system representation

Economic dispatch (ED) problems have recently been solved by artificial neural network approaches. Systems based on artificial neural networks have high computational rates due to the use of a massive number of simple processing elements and the high degree of connectivity between these elements. The ability of neural networks to realize some complex non-linear function makes them attractive for system optimization. All ED models solved by neural approaches described in the literature fail to represent the transmission system. Therefore, such procedures may calculate dispatch policies, which do not take into account important active power constraints. Another drawback pointed out in the literature is that some of the neural approaches fail to converge efficiently toward feasible equilibrium points. A modified Hopfield approach designed to solve ED problems with transmission system representation is presented in this paper. The transmission system is represented through linear load flow equations and constraints on active power flows. The internal parameters of such modified Hopfield networks are computed using the valid-subspace technique. These parameters guarantee the network convergence to feasible equilibrium points, which represent the solution for the ED problem. Simulation results and a sensitivity analysis involving IEEE 14-bus test system are presented to illustrate efficiency of the proposed approach. (C) 2004 Elsevier Ltd. All rights reserved.

Single-phase high power-factor boost ZCS pre-regulator operating in critical conduction mode

This paper presents a new single-phase interleaved high power factor boost pre-regulator operating in critical conduction mode, where the switches and boost diode performing zero-current commutations during its turn-off, eliminating the disadvantages related to the reverse recovery losses and electromagnetic interference problems of the boost diode, when operating in the continuous conduction mode. The interleaving technique is applied in the power cell, providing a significant input current ripple reduction in comparison to discontinuous mode of operation, due to its input current continuous conduction operation. This paper presents a complete modeling for the converter operating in critical conduction mode, resulting in an improved design procedure for interleaved techniques with high input power factor, a complete design procedure, and main simulation results from a design example with two interleaved cells rated at 1kW, 400V output voltage and 220V rms input voltage.

A novel single-phase ZCS-PWM high-power-factor boost rectifier

This paper presents a novel single-phase high-power-factor (HPF) pulsewidth-modulated (PWM) boost rectifier featuring soft commutation of the active switches at zero current (ZC), It incorporates the most desirable properties of conventional PWM and soft-switching resonant techniques.The input current shaping is achieved with average current mode control and continuous inductor current mode.This new PWM converter provides ZC turn on and turn off of the active switches, and it is suitable for high-power applications employing insulated gate bipolar transistors (IGBT's),The principle of operation, the theoretical analysis, a design example, and experimental results from a laboratory prototype rated at 1600 W with 400-Vdc output voltage are presented. The measured efficiency and the power factor were 96.2% and 0.99%, respectively, with an input current total harmonic distortion (THD) equal to 3.94%, for an input voltage with THD equal to 3.8%, at rated load.

Novel high-power-factor ZCS-PWM preregulators

This paper introduces novel zero-current-switching (ZCS) pulsewidth-modulated (PWM) preregulators based on a new soft-commutation cell, suitable for insulated gate bipolar transistor applications. The active switches in these proposed rectifiers turn on in zero current and turn off in zero current-zero voltage. In addition, the diodes turn on in zero voltage and their reverse-recovery effects over the active switches are negligible. Moreover, based on the proposed cell, an entire family of de-to-de ZCS-PWM converters can be generated, providing conditions to obtain naturally isolated converters, for example, derived buck-boost, Sepic. and Zeta converters. The novel ac-to-dc ZCS-PWM boost and Zeta preregulators are presented in order to verify the operation of this soft-commutation cell, In order to minimize the harmonic contents of the input current, increasing the ac power factor, the average-current-mode control is used, obtaining preregulators with ac power factor near unity and high efficiency at wide load range. The principle of operation, theoretical analysis, design example, and experimental results from test units for the novel preregulators are presented. The new boost preregulator was designed to nominal values of 1.6 kW output power, 220 V(rms) input voltage, 400 V(dc) output voltage, and operating at 20 kHz. The measured efficiency and power factor of the new ZCS-PWM boost preregulator were 96.7% and 0,99, respectively, with an input current total harmonic distortion (THD) equal to 3.42% for an input voltage with THD equal to 1.61%, at rated load, the new ZCS-PWM Zeta preregulator was designed to voltage step-down operation, and the experimental results were obtained from a laboratory prototype rated at 500 W, 220 V(rm), input voltage, 110 V(dc) output voltage, and operating at 50 kHz. The measured efficiency of the new ZCS-PWM Zeta preregulator is approximately 96.9% and the input power factor is 0.98, with an input current THD equal to 19.07% while the input voltage THD is equal to 1.96%, at rated load.