Pitch Angle Control for a Small-Scale Darrieus Vertical Axis Wind Turbine with Straight Blades (H-type VAWT)

Unlike the horizontal axis wind turbines, only a few studies have been conducted recently to improve the performance of a Darrieus Vertical Axis Wind Turbine with straight blades (H-type VAWT). Pitch angle control technique is used to enhance the performance of an H-type VAWT in terms of power output and self-starting capability. This thesis aims to investigate the performance of an H-type VAWT using an intelligent blade pitch control system. Computational Fluid Dynamics (CFD) is used to determine the optimum pitch angles and study their effects on the aerodynamic performance of a 2D H-type VAWT at different Tip Speed Ratios (TSRs) by calculating the power coefficient (Cp). The results obtained from the CFD model are used to construct the aerodynamic model of an H-type VAWT rotor, which is required to design an intelligent pitch angle controller based on Multi-Layer Perceptron Artificial Neural Networks (MLP-ANN) method. The performance of the blade pitch controller is investigated by adding a conventional controller (PID) to the MLP-ANN controller (i.e., Hybrid controller). For stability analysis, an H-type VAWT is modeled in nonlinear state space by determining the mathematical models for an H-type VAWT components along with Hybrid control scheme. The effectiveness of proposed pitch control system and the CFD results are validated by building an H-type VAWT prototype. This prototype is tested outdoor extensively at different wind conditions for both fixed and variable pitch angle configurations. Results demonstrate that the blade pitching technique enhanced the performance of an H-type VAWT in terms of power output by around 22%

Entropy and Exergy in Renewable Energy

Lovelock identified Newcomen’s atmospheric steam engine as the start of Anthropocene with these words: “…there have been two previous decisive events in the history of our planet. The first was … when photosynthetic bacteria first appeared [conversing sunlight to usable energy]. The second was in 1712 when Newcomen created an efficient machine that converted the sunlight locked in coal directly into work.” This book is about the necessity of energy transition toward renewables that convert sunlight diurnally, thus a sustainable Anthropocene. Such an energy transition is equally momentous as that of the kick start of the second Industrial Revolution in 1712. Such an energy transition requires “it takes a village” collective effort of mankind; the book is a small part of the collective endeavor

Application of PSO for optimization of power systems under uncertainty

The primary objective of this dissertation is to develop a black box optimization tool. The algorithm should be able to solve complex nonlinear, multimodal, discontinuous and mixed-integer power system optimization problems without any model reduction. Although there are many computational intelligence (CI) based algorithms which can handle these problems, they require intense human intervention in the form of parameter tuning, selection of a suitable algorithm for a given problem etc. The idea here is to develop an algorithm that works relatively well on a variety of problems with minimum human effort. An adaptive particle swarm optimization algorithm (PSO) is presented in this thesis. The algorithm has special features like adaptive swarm size, parameter free update strategies, progressive neighbourhood topologies, self learning parameter free penalty approach etc. The most significant optimization task in the power system operation is the scheduling of various generation resources (Unit Commitment, UC). The current practice used in UC modelling is the binary approach. This modelling results in a high dimension problem. This in turn leads to increased computational effort and decreased efficiency of the algorithm. A duty cycle based modelling proposed in this thesis results in 80 percent reduction in the problem dimension. The stern uptime and downtime requirements are also included in the modelling. Therefore, the search process mostly starts in a feasible solution space. From the investigations on a benchmark problem, it was found that the new modelling results in high quality solutions along with improved convergence. The final focus of this thesis is to investigate the impact of unpredictable nature of demand and renewable generation on the power system operation. These quantities should be treated as a stochastic processes evolving over time. A new PSO based uncertainty modelling technique is used to abolish the restrictions imposed by the conventional modelling algorithms. The stochastic models are able to incorporate the information regarding the uncertainties and generate day ahead UC schedule that are optimal to not just the forecasted scenario for the demand and renewable generation in feed but also to all possible set of scenarios. These models will assist the operator to plan the operation of the power system considering the stochastic nature of the uncertainties. The power system can therefore optimally handle huge penetration of renewable generation to provide economic operation maintaining the same reliability as it was before the introduction of uncertainty

Recent Development of Hybrid Renewable Energy Systems

Abstract: The use of renewable energies continues to increase. However, the energy obtained from renewable resources is variable over time. The amount of energy produced from the renewable energy sources (RES) over time depends on the meteorological conditions of the region chosen, the season, the relief, etc. So, variable power and nonguaranteed energy produced by renewable sources implies intermittence of the grid. The key lies in supply sources integrated to a hybrid system (HS)

Indoor air Quality and Its Effects on Health in Urban Houses of Indonesia: A case study of Surabaya

There is a possibility that the sick building syndrome has already spread widely among the newly constructed apartments in major cities of Indonesia. This study investigates the current conditions of indoor air quality, focusing especially on formaldehyde and TVOC, and their effects on health among occupants in the urban houses located in the city of Surabaya. A total of 471 respondents were interviewed and 82 rooms were measured from September 2017 to January 2018. The results indicated that around 50% of the respondents in the apartments showed some degrees of chemical sensitivity risk. More than 60% of the measured formaldehyde levels in the apartments exceeded the WHO standard, 0.08 ppm. The respondents living in rooms with higher mean formaldehyde values tended to have higher multiple chemical sensitivity risk scores. KEYWORDS: Indoor air quality, Sick building syndrome, QEESI, Formaldehyde, Developing countrie