Globally, solar energy is expected to play a significant role in the changing face of energy economies in the near future. However, the variability of this resource has been the main barrier for solar energy development in most locations around the world. This paper investigated the distribution and variability of solar radiation using the a 10-year (2006 to 2015) data collected at Sørås meteorological station located at latitude 59° 39′ N and longitude 10° 47′E, about 93.3 m above sea level (about 30 km from Oslo), in south-eastern part of Norway. It is found that on annual basis, the total number of days with a global solar radiation of less than 1 kWh/(m2·d) is 120 days while the total number of days with an expected global solar radiation greater than 3 kWh/(m2·d) is 156 days (42.74%) per year. The potential energy output from a horizontally placed solar collector in these 156 days is approximately 75% of the estimated annual energy output. In addition, it is found that the inter-annual coefficient of variation of the global solar radiation is 4.28%, while that of diffuse radiation is 4.96%.
This paper presents a new neural network based model reference adaptive system (MRAS) to solve low speed problems for estimating rotor resistance in vector control of induction motor (IM). The MRAS using rotor flux as the state variable with a two layer online trained neural network rotor flux estimator as the adaptive model (FLUX-MRAS) for rotor resistance estimation is popularly used in vector control. In this scheme, the reference model used is the flux estimator using voltage model equations. The voltage model encounters major drawbacks at low speeds, namely, integrator drift and stator resistance variation problems. These lead to a significant error in the estimation of rotor resistance at low speed. To address these problems, an offline trained NN with data incorporating stator resistance variation is proposed to estimate flux, and used instead of the voltage model. The offline trained NN, modeled using the cascade neural network, is used as a reference model instead of the voltage model to form a new scheme named as “NN-FLUX-MRAS.” The NN-FLUX-MRAS uses two neural networks, namely, offline trained NN as the reference model and online trained NN as the adaptive model. The performance of the novel NN-FLUX-MRAS is compared with the FLUX-MRAS for low speed problems in terms of integral square error (ISE), integral time square error (ITSE), integral absolute error (IAE) and integral time absolute error (ITAE). The proposed NN-FLUX-MRAS is shown to overcome the low speed problems in Matlab simulation.
This paper proposes an optimal dynamic reserve activation plan after the occurrence of an emergency situation (generator/transmission line outage, load increase or both). An optimal plan is developed to handle the emergency, using the coordinated action of fast and slow reserves, for secure operation with minimum overall cost. It considers the reserves supplied by the conventional thermal generators (spinning reserves), hydro power units and load demands (demand-side reserves). The optimal backing down of costly/fast reserves and bringing up of slow reserves in each sub-interval in an integrated manner is proposed. The proposed reserve activation approaches are solved using the genetic algorithm, and some of the simulation results are also compared using the Matlab optimization toolbox and the general algebraic modeling system (GAMS) software. The simulation studies are performed on the IEEE 30, 57 and 300 bus test systems. These results demonstrate the advantage of the proposed integrated/dynamic reserve activation plan over the conventional/sequential approach.
This paper presents the simulation of major mechanical properties of a flux reversal generator (FRG) viz., computational fluid dynamic (CFD), thermal, and vibration. A three-dimensional finite element analysis (FEA) based CFD technique for finding the spread of pressure and air velocity in air regions of the FRG is described. The results of CFD are mainly obtained to fine tune the thermal analysis. Thus, in this focus, a flow analysis assisted thermal analysis is presented to predict the steady state temperature distribution inside FRG. The heat transfer coefficient of all the heat producing inner walls of the machine are evaluated from CFD analysis, which forms the main factor for the prediction of accurate heat distribution. The vibration analysis is illustrated. Major vibration sources such as mechanical, magnetic and applied loads are covered elaborately which consists of a 3D modal analysis to find the natural frequency of FRG, a 3D static stress analysis to predict the deformation of the stator, rotor and shaft for different speeds, and an unbalanced rotor harmonic analysis to find eccentricity of rotor to make sure that the vibration of the rotor is within the acceptable limits. Harmonic analysis such as sine sweep analysis to identify the range of speeds causing high vibrations and steady state vibration at a mode frequency of 1500 Hz is presented. The vibration analysis investigates the vibration of the FRG as a whole, which forms the contribution of this paper in the FRG literature.
Smart technologies when used in the traditional grid infrastructure will provide a different environment and working conditions in the grid by bringing the required smartness into the grid, called the smart grid. The smart grid can play a major role in the upcoming days to come because there is a necessity to integrate coordinated renewable energy resources into the grid and to operate the grids at a higher efficiency considering many aspects including reliability of the supply. Apart from this, there is a necessity to manage the demand supply gap in the smart grid by optimally scheduling the generators or by effectively scheduling the demand side resources instead of going for the traditional methods like partial or full load shedding. This paper presents an overview on the present state-of-the-art of smart grid technologies and broadly classifies the papers referred into two major areas, papers based on improvement of operational efficiency in smart grids and papers based on smartness in maintaining the demand supply gap. Some of the papers projected in this work also give a brief overview of the necessity of the smart grid.
Considering the fact that customers of large commercial buildings have the characteristics of the higher density and randomness, this paper presented an air-conditioning cooling load prediction method based on weather forecast and internal occupancy density. The multiple linear feedback regression model was applied to predict, with precision, the air conditioning cooling load. Case analysis showed that the largest mean relative error of hourly and the daily predicting cooling load maximum were 18.1% and 5.14%, respectively.
The recent trend in light emitting diode or LED lighting applications and their claimed energy saving capabilities together with their overall attractiveness has us all convinced that they really are a greener alternative to the compact fluorescent lights or CFL. As convincing as it seems, the actual energy saving capabilities of LEDs are yet to be proven scientifically or at the least, on an empirical level when compared to CFLs. This paper tackles the issue with the use of a solar cell by evaluating the photovoltaic current and voltage generated by the solar cell subjected to each lighting system. Graphical representations are drawn and a conclusion is then reached based on the amount of power generation in the solar cells in order to determine the energy saving capabilities of each lighting system and its efficiency. From the result, it has been found that an LED is 3.7 times more power efficient than a CFL based light source of equal wattage.
A dual input LED lighting scheme with constant illumination is proposed in this paper. The scheme employs a photovoltaic array as the first input and a battery as the second one. A microcontroller is programmed to operate a changeover switch as well as a DC-DC converter for uninterrupted and constant illumination in work place. The scheme is suitable for conference halls, laboratories, clean rooms, marriage halls, theaters, etc. The complete modeling, design and experimentation of the proposed scheme are explained and the economic viability of the scheme is justified.
In this paper a novel method for reliability prediction and validation of nuclear power units in service is proposed. The equivalent availability factor is used to measure the reliability, and the equivalent availability factor deducting planed outage hours from period hours and maintenance factor are used for the measurement of inherent reliability. By statistical analysis of historical reliability data, the statistical maintenance factor and the undetermined parameter in its numerical model can be determined. The numerical model based on the maintenance factor predicts the equivalent availability factor deducting planed outage hours from period hours, and the planed outage factor can be obtained by using the planned maintenance days. Using these factors, the equivalent availability factor of nuclear power units in the following 3 years can be obtained. Besides, the equivalent availability factor can be predicted by using the historical statistics of planed outage factor and the predicted equivalent availability factor deducting planed outage hours from period hours. The accuracy of the reliability prediction can be evaluated according to the comparison between the predicted and statistical equivalent availability factors. Furthermore, the reliability prediction method is validated using the nuclear power units in North American Electric Reliability Council (NERC) and China. It is found that the relative errors of the predicted equivalent availability factors for nuclear power units of NERC and China are in the range of –2.16% to 5.23% and –2.15% to 3.71%, respectively. The method proposed can effectively predict the reliability index in the following 3 years, thus providing effective reliability management and maintenance optimization methods for nuclear power units.