Three-dimensional micro-siting of a windfarm - A CFD based analysis : A potential wind farm site in Norway, Kylland, has been analysed. An innovative 3D approach for micro-siting the wind turbines is applied together with CFD methods by using the wind farm design tool WindSim

Master's thesis in Renewable energy (ENE500

Wind farm repowering optimization: a techno‐economic‐aesthetic approach

When a wind farm (WF) approaches the end of its life cycle, repowering is another opportunity for wind energy to prove its value. This paper proposes an optimization framework to guide the WF repowering, considering the power generation, the economic cost, and the aesthetic of the WF when various types of new wind turbines (WTs) are added. When calculating the wake deficits inside the WF, a three‐dimensional (3‐D) Gaussian wake model is applied which considers the height differences among the new WTs. A harmony pattern metric is used to assess the visual impact of the rebuilt WF. This optimization problem is formulated as an integer programming (IP) problem and is tackled by the integer particle swarm optimization (IPSO) algorithm. The wind data used for this optimization procedure is predicted by the auto‐regressive (AR) model. The case study on the OWEZ WF verifies the effectiveness of the proposed method. It is also validated that the application of predicted wind data is better than the historical data for WF repowering optimization.National Natural Science Foundation of Chin

Better wind forecasting using Evolutionary Neural Architecture search driven Green Deep Learning

Climate Change heavily impacts global cities, the downsides of which can be minimized by adopting renewables like wind energy. However, despite its advantages, the nonlinear nature of wind renders the forecasting approaches to design and control wind farms ineffective. To expand the research horizon, the current study a) analyses and performs statistical decomposition of real-world wind time-series data, b) presents the application of Long Short-Term Memory (LSTM) networks, Nonlinear Auto-Regressive (NAR) models, and Wavelet Neural Networks (WNN) as efficient models for accurate wind forecasting with a comprehensive comparison among them to justify their application and c) proposes an evolutionary multi-objective strategy for Neural Architecture Search (NAS) to minimize the computational cost associated with training and inferring the networks which form the central theme of Green Deep Learning. Balancing the trade-off between parsimony and prediction accuracy, the proposed NAS strategy could optimally design NAR, WNN, and LSTM models with a mean test accuracy of 99%. The robust methodologies discussed in this work not only accurately model the wind behavior but also provide a green & generic approach for designing Deep Neural Networks

A Technical Review on Reliability and Economic Assessment Framework of Hybrid Power System with Solar and Wind Based Distributed Generators

Recent years have witnessed an upsurge in the penetration of solar and wind power. This can be chiefly attributed to worldwide climate concern and inclination towards low carbon sources. Owing to their abundant availability, solar and wind sources are projected to play a key part in de-carbonization of power sector. However, the variability of these sources and high initial cost pose a major challenge in their deployment. Thus, reliability and economic assessment is imperative to hybrid power system(HPS) with solar and wind integration. This paper tenders a survey on different aspects involved in reliability and economic assessment of HPS. Various techniques employed in uncertainty modelling of climatological parameters like solar irradiance and wind velocity have been deliberated. A detailed discussion on reliability evaluation parameters as well as techniques along with their merits and demerits has been carried out. In order to impart a sense of extensiveness to review, a discussion on economic evaluation metrics has also been presented. Further, author’s critical comments on review along with suggestions for possible research avenues has also been presented. The review presented in this paper is envisioned to facilitate a comprehensive guide towards evaluation of solar and wind energy based HP

Enhancing statistical wind speed forecasting models : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering at Massey University, Manawatū Campus, New Zealand

In recent years, wind speed forecasting models have seen significant development and growth. In particular, hybrid models have been emerging since the last decade. Hybrid models combine two or more techniques from several categories, with each model utilizing its distinct strengths. Mainly, data-driven models that include statistical and Artificial Intelligence/Machine Learning (AI/ML) models are deployed in hybrid models for shorter forecasting time horizons (< 6hrs). Literature studies show that machine learning models have gained enormous potential owing to their accuracy and robustness. On the other hand, only a handful of studies are available on the performance enhancement of statistical models, despite the fact that hybrid models are incomplete without statistical models. To address the knowledge gap, this thesis identified the shortcomings of traditional statistical models while enhancing prediction accuracy. Three statistical models are considered for analyses: Grey Model [GM(1,1)], Markov Chain, and Holt’s Double Exponential Smoothing models. Initially, the problems that limit the forecasting models' applicability are highlighted. Such issues include negative wind speed predictions, failure of predetermined accuracy levels, non-optimal estimates, and additional computational cost with limited performance. To address these concerns, improved forecasting models are proposed considering wind speed data of Palmerston North, New Zealand. Several methodologies have been developed to improve the model performance and fulfill the necessary and sufficient conditions. These approaches include adjusting dynamic moving window, self-adaptive state categorization algorithm, a similar approach to the leave-one-out method, and mixed initialization method. Keeping in view the application of the hybrid methods, novel MODWT-ARIMA-Markov and AGO-HDES models are further proposed as secondary objectives. Also, a comprehensive analysis is presented by comparing sixteen models from three categories, each for four case studies, three rolling windows, and three forecasting horizons. Overall, the improved models showed higher accuracy than their counter traditional models. Finally, the future directions are highlighted that need subsequent research to improve forecasting performance further

Quantifying the benefits and risks of real-time thermal ratings in electrical networks

PhD ThesisReal-Time Thermal Rating (RTTR) is a technology that allows the rating of electrical conductors to be estimated using real-time, local weather conditions. In many cases this leads to an increased rating with respect to conventional approaches. It also identifies some instances in which the conventional, static, rating is greater than the true rating, and is therefore potentially unsafe. The work in this thesis comprises methodologies to improve the planning and implementation of RTTR. Techniques commonly employed in the wind energy industry have been modified for use with RTTR. Computational wind simulations were employed to allow the identification of determining conductor spans, to inform network designers of the rating potential of different conductor routes, to estimate the additional wind energy that could be accommodated through the enhanced line rating and to allow informed placement of the monitoring equipment required to implement RTTR. Furthermore, the wind simulation data were also used to allow more accurate estimation of conductor ratings during operation. Probabilistic methods have been devised to estimate the level of additional load that could be accommodated through RTTR, and quantify the risk in doing so. Finally, a method has been developed to calculate the benefit RTTR can provide to system wide reliability. State sampling and sequential Monte Carlo simulations were used to evaluate the probabilistic functions associated with the ratings, the load and failures on both the existing network and the RTTR system itself. These methods combine to address fundamental barriers to the wide scale adoption and implementation of RTTR. The majority of existing research has focussed on improving technical solutions, which are of little benefit if it is not possible to quantify the benefits of RTTR before it is implemented. This work allows quantification not only of those benefits, but of the associated risks and uncertainties as well

Wind energy generation and forecasts: a case study of Darling and Vredenburg sites

This research study presents the wind resource assessment at two potential onshore wind sites at the Western Cape of South Africa for small and large scale wind energy generation. It is anticipated that by virtue of the enormous wind resources prevalent along the South Africa West Coast, it is economical and cost effective to generate electricity from the wind to offset the increasing cost of energy generation from non-renewable sources (coal-fired, nuclear, gas etc.) which are the major source of power generation. Despite the environmental benefit and economic potentials of the wind energy, its variability and the inability to accurately predict (estimate) the long term energy generation potentials usually lead to difficulties in the selection and development of a suitable wind site for any proposed wind farm project(s) in the country

WIND TURBINE CLUTTER IN WEATHER RADAR: CHARACTERIZATION AND MITIGATION

With the rapid growth of the wind power industry, many commercial utility-scale wind turbines have been built across the country. These extremely large man-made structures are reported to have negative impact on nearby radars due to their complex scattering mechanisms. Various forms of clutter effect caused by wind turbines in the radar vicinity are generally referred to as the Wind Turbine Clutter (WTC). Due to the lack of awareness on this newly recognized clutter, many wind farms have been built in the Line of Sight (LOS) coverage of operational radars, potentially affecting their performance. Weather radar is the one affected most by WTC because the target of interest is precipitation particles, which is spatially inseparable from the wind turbine within the clutter contaminated resolution volume. Our study thus focuses on analyzing the cause of different types of clutter effects by wind turbines, characterizing the radar signatures of such clutter and mitigating the clutter effect for weather radar. The Micro-Doppler signature of the WTC reveals interesting time-variant spectrum features which are closely related to the instantaneous motions of the wind turbine. The complex motions of a wind turbine can be mostly characterized by three rotations: roll, pitch and yaw. Electromagnetic (EM) characterization of such a dynamic electrical large target is challenging. Various scattering mechanisms are analyzed and the back scattered field and RCS of the wind turbine are computed using commercial EM solver and a hybrid high-frequency approximation approach developed from our study. Field measurements were carried out by deploying the mobile radar to wind farms. The measurements give us the first non-aliased Doppler spectrum of wind turbines. In order to synchronize the wind turbine motion with radar data acquisition, the Radar Wind Turbine Testbed (RWT$^2$) was developed for indoor scaled measurements, which includes the scaled wind turbine model and the scatterometer. Both frequency and time domain measurements were made to characterize the statistics of return signal from the wind turbine model. Several mitigation schemes developed from our study will be discussed, including the telemetry based method, the Adaptive Spectrum Processing (ASP) and the mitigation scheme for moment data based on the Maximum A Posterior (MAP) criteria. A thorough analysis of utilizing LOS avoidance to prevent WTC at the first place will be presented at the end

Nuevos algoritmos de soft-computing en física atmosférica

Tesis de la Universidad Complutense de Madrid, Facultad de Ciencias Físicas, leída el 12-03-2019This Ph.D. Thesis elaborates and analyzes several hybrid Soft-Computing algorithms for optimization and prediction problems in Atmospheric Physics. The core of the Thesis is a recently developed optimization meta-heuristic, the Coral Reefs Optimization Algorithm (CRO), an evolutionary-based approach which considers a population of possible solutions to a given optimization problem. It simulates different procedures mimicking real processes occurring in coral reefs in order to evolve the population towards good solutions for the problem. Alternative modifications of this algorithm lead to powerful co-evolution meta-heuristics, such as theCRO-SL, in which Substrates implementing different search procedures are included. Another modification of the algorithm leads to the CRO-SP, which considers Species in the evolutionof the population, and it is able to deal with different encodings within a single population.These approaches are hybridized with other Machine Learning and traditional algorithms such as neural networks or the Analogue Method (AM), to come up with powerful hybrid approaches able to solve hard problems in Atmospheric Physics...En esta Tesis Doctoral se elaboran y analizan en detalle diferentes algoritmos híbridos deSoft-Computing para problemas de optimización y predicción en Física de la Atmósfera. El núcleo central de la Tesis es un algoritmo meta-heurístico de optimización recientemente desarrollado, conocido como Coral Reefs Optimization algorithm (CRO). Este algoritmo pertenece a la familia de la Computación Evolutiva, de forma que considera una población de solucionesa un problema concreto, y simula los diferentes procesos que ocurren en un arrecife de coralpara evolucionar dicha población hacia la solución óptima del problema. Recientemente se han propuesto diferentes versiones del algoritmo CRO básico para obtener mecanismos potentes de optimización co-evolutiva. Una de estas modificaciones es el CRO-SL, en la que se definen un conjunto de Sustratos en el algoritmo, de manera que cada sustrato simula un mecanismo de evolución diferente, que son aplicados a la vez en una única población. Otra modificación hadado lugar al conocido como CRO-SP, un algoritmo donde se definen diferentes Especies, capaz de manejar varias codificaciones para un mismo problema a la vez. Estas versiones del CRO han sido hibridadas con varias técnicas de Aprendizaje Máquina, tales como varios tipos de redes neuronales de entrenamiento rápido, sistemas de aprendizaje tales como Máquinas de Vectores Soporte, o sistemas de predicción vinculados totalmente al área de la Física Atmosférica, tales como el Método de los Análogos (AM). Los algoritmos híbridos obtenidos son muy robustos y capaces de obtener excelentes soluciones en diferentes problemas donde han sido probados...Fac. de Ciencias FísicasTRUEunpu