Engineering

So far several controllers such as conventional PI, PID controllers have been utilized in FACTS controllers
control system for active and reactive power control in HVAC transmission lines, and hence oscillations
occurred in power system are damped. In this paper, Fuzzy controller is used in the control system of one of
the FACTS member i.e. static synchronous series compensator (SSSC) to increase its response
appropriately, especially in power system critical conditions. In this paper it is tried to obtain the goals of
damping transient oscillations and reaching steady state quickly as well as pursuing the reference values
properly. Simulation results showed that. Simulations have been performed in MATLAB/SIMULINK
environment. To show the effectiveness of the proposed controller, its results have been compared with the
two other controllers in Peak and Fall values also Settling time for which Fuzzy controller could lead to
better results in all parameters.

Keywords: FACTS, Fuzzy controller, Static synchronous series compensator, Transient oscillations, Power
system stability

Construction progress monitoring has been perceived as one of the key factors that prompt the achievement of a construction project. However, assessing the progress is time consuming, costly and obliges specialized personnel to reduce disagreements and approximate the actual performance to the original plan as close as possible. Image processing is a promising method that has been developed for automated monitoring of construction projects. It has attracted increasing attention for progress monitoring, quality assurance and work space analyses. Nonetheless, remarkable drawbacks still remain in image processing, particularly for outdoor environment such as construction progress monitoring. The principle downside of image processing goes back to the image resolution. Ambient lightning condition significantly affects the image quality which does affect the accuracy of data, extracted from related images. Much research strives to reduce the level of errors for data extraction but so far none has been able to deliver complete satisfactory and reliable result. In this research a novel approach based on thermal image analysis is presented. The new method consists of three phases: First, collecting the thermal and original images by utilizing Infrared-Camera. Second, estimating the position of captured images by the use of wireless sensor network implemented in the work space. Finally, the 3D plan will be updated automatically in the Building Information Modeling (BIM) software. The preliminary experimental results from an actual concrete building construction site show the feasibility of inferring the actual state of progress by the use of thermal images to overcome the limitation of vision monitoring. 

AUTOMATED CONSTRUCTION PROGRESS MONITORING USING THERMAL IMAGES AND WIRELESS SENSOR NETWORKS. Available from: https://www.researchgate.net/publication/275024236_AUTOMATED_CONSTRUCTION_PROGRESS_MONITORING_USING_THERMAL_IMAGES_AND_WIRELESS_SENSOR_NETWORKS#share [accessed Sep 11, 2015].

Nowadays, thermal comfort analysis is rapidly gaining popularity in social economy. Well planned air conditioning systems may provide healthy and comfortable indoor environment where poor design may result in wasting energy. In addition, it could cause some health problems for a long period. Thus, it is essential to analyze the performance of air conditioners in terms of the comfort level of occupants in buildings. Computational Fluid Dynamics (CFD) is a suitable solution for modeling the indoor conditions to achieve the effective design in buildings. In this paper, the performance of air conditioning system and the level of thermal comfort for occupants in an office at Xi’an Jiaotong-Liverpool University have been analyzed. Wind velocity and temperature have been simulated. Afterwards, the Predicted Mean Vote (PMV) and the Predicted Percentage Dissatisfied (PPD) are computed using the data acquired from the experimental measurements performed in the building. The results show the p...

With new changes in power systems, opportunity for new technologies has been made. Among such new technologies is Distributed Generator (DG). DGs have lots of advantages such as: reducing electricity cost, managing congestion in transmission lines, reducing loss, improving voltage profile etc. So it is forecasted that DGs will have increasing contribution in supplying the electricity demanded by customers in future. Artificial intelligence techniques are among the most common tools that are being used in DG allocation optimization problems. Genetic algorithm (GA) is one of these techniques which is an effective tool for solving optimization problem. In this paper a method is proposed for DG capacity and placement optimization in distribution systems using GA. The goal is to reduce active power loss and improve voltage profile.

Increasingly expensive and polluting sources of energy dictate the need of energy conservation in the society. The major energy consumption contribution in the operation phase of modern buildings is from their HVAC systems. Well planned HVAC systems may provide healthy and comfortable indoor environment, while poor design may result in wastage of energy. Traditionally the design of the systems is based on a constant number of occupants while the situational awareness of the system has been ignored. This paper focuses on the intelligence of the HVAC system in terms of its knowledge of the occupants of the facility in real time. Thermal comfort analysis is applied to study the indoor climate of buildings for efficient use of such HVAC systems. Computational Fluid Dynamics (CFD) is used to model and simulate the indoor conditions. Building Information Modelling (BIM) is used to be a platform to integrate all the data and apply spatial analysis and Green building standards are embedded in the BIM model to evaluate the building sustainability. Based on the results, more reasonable allocation of individual's location in a room can be provided and an automatic control of lighting, air conditioning can be suggested to optimize energy consumption based on matching occupants' comfort level.

Current air conditioning systems often rely on maximum occupancy assumptions and fixed schedules to maintain sufficient comfort level. Having knowledge regarding occupancy situation may lead to significant energy-savings in a building. Therefore, the present research proposes a method to investigate an occupancy-driven HVAC control system based on thermal comfort analysis. Computational Fluid Dynamics (CFD) is used to evaluate thermal comfort through modeling the indoor air distribution and flows. Wind velocity and temperature are simulated in several scenarios and the Predicted Mean Vote (PMV) and the Predicted Percentage Dissatisfied (PPD) are computed. The simulation results are verified through a survey asking for occupants’ real feelings and consequently thermal comfort profiles are identified, which are used for possible energy saving. Moreover, a pre-defined working schedule and historical behavior of persons are utilized to develop a pattern for predicting personal occupancy situations. Finally, all variables are imported to an intelligent system to fulfil an intelligent control of air conditioning system. The result shows a good potential to reduce energy consumption while meeting comfort requirements of occupants.

Highlights • Thermal Zones were considered by PMV-PPD model. • A Fuzzy-based approach was used to develop an individual thermal profile for each occupant. • Combination of individual profiles provided the best satisfaction range for present occupants in the office. Abstract A personal comfort assessment model predicts occupants' thermal comfort responses, specific to each individual, as an alternative to the mean response of a large population. However, current air conditioning systems rely on a fixed set-point based on the maximum occupancy assumptions which may cause discomfort for occupants. In addition, securing consistent comfort level for occupants in a shared space is challenging due to the difference of preference thermal comfort of individuals. This research proposes a method which is not only relying on individuals' survey participation but also utilizing the advantages of the comfort level modelling. Computational fluid dynamics (CFD) was used to evaluate thermal comfort through modelling of the predicted mean vote (PMV) and the predicted percentage dissatisfied (PPD) in several scenarios and the consequential thermal zones and their corresponding occupants were identified. A fuzzy-based approach is used to develop a personal thermal preference profile. Based on the present occupants in the room, a boundary of temperatures for HVAC set-points is estimated and used for controlling the HVAC system. The implementation of the proposed method shows the mean margin error of 12.95 percent for the prediction of preference temperatures of nine occupants in a shared space. The result shows that the proposed approach has a significant potential of maintaining most of the occupants in a reasonable thermal comfort range.

Security and reliability are major concerns for future power systems with distributed generation. A comprehensive evaluation of the risk associated with these systems must consider contingencies under normal environmental conditions and also extreme ones. Environmental conditions can strongly influence the operation and performance of distributed generation systems, not only due to the growing shares of renewable-energy generators installed but also for the environment-related contingencies that can damage or deeply degrade the components of the power grid. In this context, the main novelty of this paper is the development of probabilistic risk assessment and risk-cost optimization framework for distributed power generation systems, that take the effects of extreme weather conditions into account. A Monte Carlo non-sequential algorithm is used for generating both normal and severe weather. The probabilistic risk assessment is embedded within a risk-based, bi-objective optimization to find the optimal size of generators distributed on the power grid that minimize both risks and cost associated with severe weather. An application is shown on a case study adapted from the IEEE 13 nodes test system. By comparing the results considering normal environmental conditions and the results considering the effects of extreme weather, the relevance of the latter clearly emerges.