Canadian Niagara Power Glass Slide - Four Men in Tunnel

Four men standing in hydro tunnel.

Canadian Niagara Power Company - Water rushing through tunnel

Photograph of water rushing through hydro tunnel.

Canadian Niagara Power Company - Tunnel

Another photograph of several men walking through the tunnel.

Canadian Niagara Power Company - Large group photo in tunnel

Large group of men standing in the tunnel wearing boots and hard hats.

Canadian Niagara Power Company - Group in Tunnel

Group of men in the hydro tunnel with a loaded dolly and large lamp for light.

Canadian Niagara Power Company - Man in Tunnel

One male looking down tunnel.

Canadian Niagara Power Company - Water pouring into Tunnel

Three men looking down tunnel as water is pouring in.

Canadian Niagara Power Company - Four Men in the Tunnel (different angle)

Four men, same men from a previous photograph, standing in the water of the tunnel.

Canadian Niagara Power Company - Man working in rafter of tunnel

A photograph of man working high in rafter of the tunnel with ropes securing him.

Canadian Niagara Power Company - Tunnel Wall

A photograph of the hydro tunnel wall.

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 urban solar flux island: measurements from London

Solar irradiance measurements from a new high density urban network in London are presented. Annual averages demonstrate that central London receives 30 ± 10 Wm-2 less solar irradiance than outer London at midday, equivalent to 9 ± 3% less than the London average. Particulate matter and AERONET measurements combined with radiative transfer modeling suggest that the direct aerosol radiative effect could explain 33 to 40% of the inner London deficit and a further 27 to 50% could be explained by increased cloud optical depth due to the aerosol indirect effect. These results have implications for solar power generation and urban energy balance models. A new technique using ‘Langley flux gradients’ to infer aerosol column concentrations over clear periods of three hours has been developed and applied to three case studies. Comparisons with particulate matter measurements across London have been performed and demonstrate that the solar irradiance measurement network is able to detect aerosol distribution across London and transport of a pollution plume out of London.

Análise da viabilidade econômica de um sistema de aquecimento solar de água para uma empresa do setor hoteleiro

This paper presents an analysis of technical and financial feasibility of the use of a solar system for water heating in a fictitious hotel located in the Northeast region. Thereunto it is used techniques of solar collectors´ sizing and methods of financial mathematics, such as Net Present Value (NPV), Internal Rate of Return (IRR) and Payback. It will also be presented a sensitivity analysis to verify which are the factors that impact the viability of the solar heating. Comparative analysis will be used concerning three cities of distinct regions of Brazil: Curitiba, Belém and João Pessoa. The viability of using a solar heating system will be demonstrated to the whole Brazil, especially to the northeast region as it is the most viable for such an application of solar power because of its high levels of solar radiation. Among the cities examined for a future installation of solar heating systems for water heating in the hotel chain, João Pessoa was the one that has proved more viable.

A novel single-phase hpf hybrid rectifier suitable for front-end trolleybus systems

This paper proposal presents the development and the experimental analysis of a new single-phase hybrid rectifier structure with high power factor (PF) and low harmonic distortion of current (THDI), suitable for application in traction systems of electrical vehicles pulled by electrical motors (trolleybus), which are powered by urban distribution network. This front-end rectifier structure is capable of providing significant improvements in trolleybuses systems and in the urban distribution network costs, and efficiency. The proposed structure is composed by an ordinary single-phase diode rectifier with parallel connection of a switched converter. It is outlined that the switched converter is capable of composing the input line current waveform assuring high power factor (HPF) and low THDI, as well as ordinary front-end converter. However, the power rating of the switched converter is about 34% of the total output power, assuring robustness and reliability. Therefore, the proposed structure was named single-phase HPF hybrid rectifier. A prototype rated at 15kW was developed and analyzed in laboratory. It was found that the input line current harmonic spectrum is in accordance with the harmonic limits imposed by IEC61000-3-4. The principle of operation, the mathematical analysis, the PWM control strategy, and experimental results of a 15kW prototype are also presented in this paper. © 2009 IEEE.

Proposal of a hybrid rectifier structure with HPF and low THD suitable for front-end trolleybuses systems supplied by AC distribution networks

This paper presents the development and the experimental analysis of a new single-phase hybrid rectifier structure with high power factor (PF) and low harmonic distortion of current (THDI), suitable for application in traction systems of electrical vehicles pulled by electrical motors (trolleybus), which are powered by urban distribution network. This front-end rectifier structure is capable of providing significant improvements in trolleybuses systems and in the urban distribution network costs, and efficiency. The proposed structure is composed by an ordinary single-phase diode rectifier with parallel connection of a switched converter. It is outlined that the switched converter is capable of composing the input line current waveform assuring high power factor (HPF) and low THDI, as well as ordinary front-end converter. However, the power rating of the switched converter is about 34% of the total output power, assuring robustness and reliability. Therefore, the proposed structure was named single-phase HPF hybrid rectifier. A prototype rated at 15kW was developed and analyzed in laboratory. It was found that the input line current harmonic spectrum is in accordance with the harmonic limits imposed by IEC61000-3-4. The principle of operation, the mathematical analysis, the PWM control strategy, and experimental results are also presented in this paper. © 2009 IEEE.