Annual Energy Outlook 2008 With Projections to 2030

ライブラリー Annual Energy Outlook 2000

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In the last few years, the application of Model Predictive Control (MPC) for energy management in buildings has received significant attention from the research community. MPC is becoming more and more viable because of the increase in computational power of building automation systems and the availability of a significant amount of monitored building data. MPC has found successful implementation in building thermal regulation, fully exploiting the potential of building thermal mass. Moreover, MPC has been positively applied to active energy storage systems, as well as to the optimal management of on-site renewable energy sources. MPC also opens up several opportunities for enhancing energy efficiency in the operation of Heating Ventilation and Air Conditioning (HVAC) systems because of its ability to consider constraints, prediction of disturbances and multiple conflicting objectives, such as indoor thermal comfort and building energy demand. Despite the application of MPC algorithms in building control has been thoroughly investigated in various works, a unified framework that fully describes and formulates the implementation is still lacking. Firstly, this work introduces a common dictionary and taxonomy that gives a common ground to all the engineering disciplines involved in building design and control. Secondly the main scope of this paper is to define the MPC formulation framework and critically discuss the outcomes of different existing MPC algorithms for building and HVAC system management. The potential benefits of the application of MPC in improving energy efficiency in buildings were highlighted.

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Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities

This paper investigates heat pump systems in smart grids, focussing on fields of application and control approaches that have emerged in academic literature. Based on a review of published literatu ...

https://zenodo.org/record/895978/files/article.pdf

On heat pumps in smart grids: A review

A Review to Diagnose Faults Related to Three-Phase Industrial Induction Motors

This paper presents a preliminary simulation study of Lithium-ion batteries. The dynamic model of Li-ion batteries was created and the model was validated with previous results for different temperatures. It found that the developed model produce the results acquired with previous results. The Li-ion battery characteristics were simulated as voltage profiles for two different condition of initial State of Charge (SOC) and for two different values of temperature. The usable operating time and the battery were found to vary with SOC initial and temperature. It is also found that the usable run time of the battery increases with increase in SOC initial and decrease in temperature. The Li-ion discharge battery model was simulated for the Hybrid electrical Vehicle (HEV). The simulation results give a framework of a nonlinear dynamic performance of the Li-ion battery pack for the electrical scooter.

Capacity study of lithium ion battery for hybrid electrical vehicle (HEV) a simulation approach

This chapter provides a comprehensive analysis of noninvasive methods to diagnose stator winding insulation faults of an induction motor. Further, a novel noninvasive method is proposed to diagnose the root cause of winding failure due to unbalanced voltage to avoid catastrophic failure. Therefore, a winding function approach is utilized to derive an analytical expression for stator winding distribution andmagnetomotive force (MMF). This tactic qualifies the conductor segment that generates MMF, and it also helps to analyze a healthy current spectrum. One can easily observe higher order harmonics in current spectrum; therefore, a new series of rotor harmonics is introduced to diagnose unbalanced supply. The locus of these harmonics is dependent on the poles, rotor bars, and slip. Due to the rapid complexity in industrial plants, it is inconceivable to continue human inspection to diagnose the faults. Thus, to avoid human inspection, in addition to new series of rotor harmonic, a fully automatic method based on neural network is proposed. This method not only diagnoses unbalanced voltage but it also recognize the percentage of unbalanced voltage by use of feed-forward multilayer perceptron (MLP) trained by back propagation. Finally, the experimental results shows the validation of this research work proposed method.

/pdf/noninvasive-methods-for-condition-monitoring-and-electrical-39t0hgjodu.pdf

Noninvasive Methods for Condition Monitoring and Electrical Fault Diagnosis of Induction Motors

Optimization for the planning of distributed renewable generators using genetic algorithm

Reducing energy consumption is one of the most important for optimal electric-driven chiller operation. Therefore, even small reduction in power consumption will achieve significant energy savings. This paper adopts improved particle swarm optimization (IPSO), which is aiming to reduce energy consumption, and improve the performance of chillers. The method has been validated by real case study, and the results have demonstrated the effectiveness for saving energy and kept the cooling demand at satisfactory level.

Nursyarizal Mohd Nor

Papers

A Review to Diagnose Faults Related to Three-Phase Industrial Induction Motors

Capacity study of lithium ion battery for hybrid electrical vehicle (HEV) a simulation approach

Noninvasive Methods for Condition Monitoring and Electrical Fault Diagnosis of Induction Motors

Optimization for the planning of distributed renewable generators using genetic algorithm

Optimal Chiller Loading Using Improved Particle Swarm Optimization