Nature-Inspired Approach Using Seasonal Comparison of Wind Speed With Spectral and Statistical Analysis

Seasonal analysis of wind speed includes elements of its evaluation and analysis for wind energy production in complex geographical areas. These analyses require wind energy systems to be set up, integrated, operated, and designed according to seasonal differences. Istanbul wind speed data were collected hourly and analyzed seasonally. When the results of the analysis are examined, no significant increase in seasonal transitions was observed, while certain changes were observed between summer and winter. Here, statistical analysis, Weibull distribution function, and signal processing-based PSD analysis for wind speed is performed. In addition, correlation analysis was made between the seasons. Although significant results were obtained in signal-based analyses, results were obtained for seasonal transitions in correlation analyses. Seasonal spectral densities were calculated in the spectral analysis of wind speed data. This study has important implications in terms of extraction of seasonal characteristics of wind speed, resource assessment, operation, investment, and feasibility.

Comparative Analysis of Various Types of Control Techniques for Wind Energy Conversion System

The interest towards renewable energy has been enhanced due to zero pollutant emission. Considering the present scenario, wind as a renewable source of energy is highly recommended. As it is freely available and free from pollution, wind can effectively play a role for energy generation. This can produce quality power during grid integrations as the load demands. Due to rapid variations in wind speed, wind energy systems need highly synchronized and powerful controller techniques for power regulations to overcome transients, voltage sags, and swells. A suitable and responsive controller is essential for power generation from wind energy. The controllers for wind energy system are categorized into five controller designs according to their locations to control the demand of the turbine system during grid integrations. In this chapter, various controller designs and implementations are highlighted with reference to previous works and existing studies.

A comparative study of wind turbine-generator modeling techniques: Physical modeling, subspace identification, and dynamic neural networks

The increase of the favorable impacts of wind energy on the environment and the global energy requires overall understanding of the modeling methods that are commonly used for time-based simulation of wind energy systems. This paper introduces a comprehensive comparison of three salient modeling techniques of wind energy conversion systems, which are: the physical modeling, subspace system identification, and Dynamic Neural Network (ANN). The models have been created with the different modeling philosophies with the aid of historical data-sets representing four apart days of operation. The real system incorporates (TWT-1.65) type Wind-Turbine intergated with Multi-Pole Synchronous Generators (MPSG). The compariosn provides some crucial answers to the concerns of which technique is suited for an application, consequently, the comparison includes quantitative and qualitative measures. This article can be considered as a brief guide for future researchers to have thorough understanding of the modeling concepts in the field of wind engineering.

Flight Stability of Rigid Wing Airborne Wind Energy Systems

The flight mechanics of rigid wing Airborne Wind Energy Systems (AWESs) is fundamentally different from the one of conventional aircrafts. The presence of the tether largely impacts the system dynamics, making the flying craft to experience forces which can be an order of magnitude larger than those experienced by conventional aircrafts. Moreover, an AWES needs to deal with a sustained yet unpredictable wind, and the ensuing requirements for flight maneuvers in order to achieve prescribed control and power production goals. A way to maximize energy capture while facing disturbances without requiring an excessive contribution from active control is that of suitably designing the AWES craft to feature good flight dynamics characteristics. In this study, a baseline circular flight path is considered, and a steady state condition is defined by modeling all fluctuating dynamic terms over the flight loop as disturbances. In-flight stability is studied by linearizing the equations of motion on this baseline trajectory. In populating a linearized dynamic model, analytical derivatives of external forces are computed by applying well-known aerodynamic theories, allowing for a fast formulation of the linearized problem and for a quantitative understanding of how design parameters influence stability. A complete eigenanalysis of an example tethered system is carried out, showing that a stable-by-design AWES can be obtained and how. With the help of the example, it is shown how conventional aircraft eigenmodes are modified for an AWES and new eigenmodes, typical of AWESs, are introduced and explained. The modeling approach presented in the paper sets the basis for a holistic design of AWES that will follow this work.

Optimal Sizing of Battery-Integrated Hybrid Renewable Energy Sources with Ramp Rate Limitations on a Grid Using ALA-QPSO

Higher penetration of variable renewable energy sources into the grid brings down the plant load factor of thermal power plants. However, during sudden changes in load, the thermal power plants support the grid, though at higher ramping rates and with inefficient operation. Hence, further renewable additions must be backed by battery energy storage systems to limit the ramping rate of a thermal power plant and to avoid deploying diesel generators. In this paper, battery-integrated renewable energy systems that include floating solar, bifacial rooftop, and wind energy systems are evaluated for a designated smart city in India to reduce ramping support by a thermal power plant. Two variants of adaptive-local-attractor-based quantum-behaved particle swarm optimization (ALA-QPSO) are applied for optimal sizing of battery-integrated and hybrid renewable energy sources to minimize the levelized cost of energy (LCoE), battery life cycle loss (LCL), and loss of power supply probability (LPSP). The obtained results are then compared with four variants of differential evolution. The results show that out of 427 MW of the energy potential, an optimal set of hybrid renewable energy sources containing 274 MW of rooftop PV, 99 MW of floating PV, and 60 MW of wind energy systems supported by 131 MWh of batteries results in an LPSP of 0.005%, an LCoE of 0.077 USD/kW, and an LCL of 0.0087. A sensitivity analysis of the results obtained through ALA-QPSO is performed to assess the impact of damage to batteries and unplanned load appreciation, and it is found that the optimal set results in more energy sustainability.