Query: wind power generation

title: Automated power management strategy for wind power generation system using pitch angle controller
abstract: In this literature, a new automated control strategy has been developed to manage the power supply from the wind power generation system to the load. The main objective of this research work is to develop a fuzzy logic based pitch angle control and to develop a static transfer switch to make power balance between the wind power generation system and the loads. The power management control system is a progression of logic expressions, designed based on generating power and load power requirement. The outcome of this work targets at an improved power production, active and reactive power compensation and ensures system load constraints. To validate the proposed control strategy, a detailed simulation study is carried out on a 9 MW wind farm simulation simulated in MATLAB/Simulink environment.

title: An improved radial basis function neural network control strategy based maximum power point tracking controller for wind power generation system
abstract: This literature presents an improved maximum power point tracking (MPPT) controller based on radial basis function neural network (RBFNN) control strategy to extract optimal power for wind power generation system. The proposed RBFNN controller is trained online using gradient descent algorithm and its network learning rate modification is carried out by the modified particle swarm optimization algorithm. The proposed MPPT controller uses optimal torque control methodology to extract optimal power available in the wind by upholding the generated torque at an optimal level. The most promising aspects of the proposed controller are that it not only extracts maximum available power from wind, but it also rapidly responses to the change in wind speeds and maintains converter with negligible converter losses. To evaluate the performance of the proposed MPSO RBFNN based MPPT controller, an extensive simulation study and experimental analysis is performed. The attained results confirm the enhanced performance of the proposed MPPT controller.

title: Wind power generation: A review and a research agenda
abstract: The use of renewable energy resources, especially wind power, is receiving strong attention from governments and private institutions, since it is considered one of the best and most competitive alternative energy sources in the current energy transition that many countries around the world are adopting. Wind power also plays an important role by reducing greenhouse gas emissions and thus attenuating global warming. Another contribution of wind power generation is that it allows countries to diversify their energy mix, which is especially important in countries where hydropower is a large component. The expansion of wind power generation requires a robust understanding of its variability and thus how to reduce uncertainties associated with wind power output. Technical approaches such as simulation and forecasting provide better information to support the decision making process. This paper provides an overview of how the analysis of wind speed/energy has evolved over the last 30 years for decision making processes. For this, we employed an innovative and reproducible literature review approach called Systematic Literature Network Analysis (SLNA) The SLNA was performed considering 145 selected articles from peer reviewed journals and through them it was possible to identify the most representative approaches and future trends. Through this analysis, we identified that in the past 10 years, studies have focused on the use of Measure Correlate Predict (MCP) models, first using linear models and then improving them by applying density or kernel functions, as well as studies with alternative techniques, like neural networks or other hybrid models. An important finding is that most of the methods aim to assess wind power generation potential of target sites, and, in recent years the most used approaches are MCP and artificial neural network methods.

title: Seasonal forecasts of wind power generation
abstract: The energy sector is highly dependent on climate variability for electricity generation, maintenance activities and demand. In recent years, a few climate services have appeared that provide tailored information for the energy sector. In particular, seasonal climate predictions of wind speed have proven useful to the wind power industry. However, most of the service users are ultimately interested in forecasts of electricity generation instead of wind. Although power generation depends on many factors other than wind conditions, the capacity factor is a suitable indicator to quantify the impact of wind variability on production. In this paper a methodology to produce seasonal predictions of capacity factor for a range of turbine classes is proposed for the first time. The strengths and weaknesses of the method are discussed and the forecast quality is evaluated for an application example over Europe.

title: Mid to long term wind and photovoltaic power generation prediction based on copula function and long short term memory network
abstract: The accurate estimation of mid to long term wind and photovoltaic power generation is important to the power grid's plan improvement, dispatching optimization, management development, and consumption enhancement. These constitute key factors for the realization of power mutual assistance and complementary dispatch of power generation in the broad area of renewable energy. However, owing to the large time scale of mid to long term prediction, the low accuracy of weather prediction, the limited data samples of historical power generation, and the significant difference between power generation prediction and short term power prediction, short term power prediction technology cannot be directly copied. Thus, the industry has not established yet an effective approach for mid to long term wind and photovoltaic power generation predictions. To solve these problems, this study proposed a method for the mid to long term wind and photovoltaic power generation prediction based on copula function and long short term memory network to achieve an effective extraction of the key meteorological factors that affect power generation owing to nonlinear effects and tendencies, and to deeply exploit the long term dependencies and tendencies from the limited available data samples. Therefore, the proposed approach is suitable for mid to long term wind and photovoltaic power generation prediction using limited data samples. Firstly, the non linear effects and tendency correlation measurements of the copula function were used to extract the key meteorological factors that influence wind and photovoltaic power generation. Secondly, independent wind/photovoltaic prediction models were established based on long short term memory network using the best input condition obtained by comparing these models to the persistence model. Additionally, the independent wind/photovoltaic models were further compared to support vector machine model with the optimal input condition. Thirdly, the joint prediction models of wind and photovoltaic power generation based on long short term memory network were established using different inputs. The persistence model and the support vector machine model were used as benchmarks to compare the elicited performances. Finally, the validity and applicability of the proposed approach were extensively evaluated using actual data from wind farms and photovoltaic power stations in China and the United States. The independent and joint power generation prediction results demonstrated that the proposed approach outperforms both the persistence model and the support vector machine model, and can have widespread applicability in limited data sample cases.

title: A Distributionally Robust Optimization Model for Unit Commitment Considering Uncertain Wind Power Generation
abstract: This paper proposes a distributionally robust optimization model for solving unit commitment (UC) problems considering volatile wind power generation. The uncertainty of wind power is captured by an ambiguity set that defines a family of wind power distributions, and the expected total cost under the worst case distribution is minimized. Compared with stochastic programming, this method may have less dependence on the data of exact probability distributions. It should also outperform the conventional robust optimization methods because some distribution information can be incorporated into the ambiguity sets to generate less conservative results. In this paper, the UC model is formulated based on the typical two stage framework, where the UC decisions are determined in a here and now manner, and the economic dispatch decisions are assumed to be wait and see, made after the observation of wind power outcomes. For computational tractability, the wait and see decisions are addressed by linear decision rule approximation, assuming that the economic dispatch decisions affinely depend on uncertain parameters as well as auxiliary random variables introduced to describe distributional characteristics of wind power generation. It is shown in case studies that this decision rule model tends to provide a tight approximation to the original two stage problem, and the performance of UC solutions may be greatly improved by incorporating information on wind power distributions into the robust model.

title: Current methods and advances in forecasting of wind power generation
abstract: Wind power generation differs from conventional thermal generation due to the stochastic nature of wind. Thus wind power forecasting plays a key role in dealing with the challenges of balancing supply and demand in any electricity system, given the uncertainty associated with the wind farm power output. Accurate wind power forecasting reduces the need for additional balancing energy and reserve power to integrate wind power. Wind power forecasting tools enable better dispatch, scheduling and unit commitment of thermal generators, hydro plant and energy storage plant and more competitive market trading as wind power ramps up and down on the grid. This paper presents an in depth review of the current methods and advances in wind power forecasting and prediction. Firstly, numerical wind prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed. Next the statistical and machine learning approach methods are detailed. Then the techniques used for benchmarking and uncertainty analysis of forecasts are overviewed, and the performance of various approaches over different forecast time horizons is examined. Finally, current research activities, challenges and potential future developments are appraised.

title: Direct Quantile Regression for Nonparametric Probabilistic Forecasting of Wind Power Generation
abstract: The fluctuation and uncertainty of wind power generation bring severe challenges to secure and economic operation of power systems. Because wind power forecasting error is unavoidable, probabilistic forecasting becomes critical to accurately quantifying the uncertainty involved in traditional point forecasts of wind power and to providing meaningful information to conduct risk management in power system operation. This paper proposes a novel direct quantile regression approach to efficiently generate nonparametric probabilistic forecasting of wind power generation combining extreme learning machine and quantile regression. Quantiles with different proportions can be directly produced via an innovatively formulated linear programming optimization model, without dependency on point forecasts. Multistep probabilistic forecasting of 10 min wind power is newly carried out based on real wind farm data from Bornholm Island in Denmark. The superiority of the proposed approach is verified through comparisons with other well established benchmarks. The proposed approach forms a new artificial neural network based nonparametric forecasting framework for wind power with high efficiency, reliability, and flexibility, which can be beneficial to various decision making activities in power systems.

title: Probabilistic Forecasting of Wind Power Generation Using Extreme Learning Machine
abstract: Accurate and reliable forecast of wind power is essential to power system operation and control. However, due to the nonstationarity of wind power series, traditional point forecasting can hardly be accurate, leading to increased uncertainties and risks for system operation. This paper proposes an extreme learning machine (ELM) based probabilistic forecasting method for wind power generation. To account for the uncertainties in the forecasting results, several bootstrap methods have been compared for modeling the regression uncertainty, based on which the pairs bootstrap method is identified with the best performance. Consequently, a new method for prediction intervals formulation based on the ELM and the pairs bootstrap is developed. Wind power forecasting has been conducted in different seasons using the proposed approach with the historical wind power time series as the inputs alone. The results demonstrate that the proposed method is effective for probabilistic forecasting of wind power generation with a high potential for practical applications in power systems.

title: Simulating European wind power generation applying statistical downscaling to reanalysis data
abstract: The growing share of electricity production from solar and mainly wind resources constantly increases the stochastic nature of the power system. Modelling the high share of renewable energy sources and in particular wind power crucially depends on the adequate representation of the intermittency and characteristics of the wind resource which is related to the accuracy of the approach in converting wind speed data into power values. One of the main factors contributing to the uncertainty in these conversion methods is the selection of the spatial resolution. Although numerical weather prediction models can simulate wind speeds at higher spatial resolution (up to 1x1km) than a reanalysis (generally, ranging from about 25km to 70km) they require high computational resources and massive storage systems: therefore, the most common alternative is to use the reanalysis data. However, local wind features could not be captured by the use of a reanalysis technique and could be translated into misinterpretations of the wind power peaks, ramping capacities, the behaviour of power prices, as well as bidding strategies for the electricity market. This study contributes to the understanding what is captured by different wind speeds spatial resolution datasets, the importance of using high resolution data for the conversion into power and the implications in power system analyses. It is proposed a methodology to increase the spatial resolution from a reanalysis. This study presents an open access renewable generation time series dataset for the EU 28 and neighbouring countries at hourly intervals and at different geographical aggregation levels (country, bidding zone and administrative territorial unit) for a 30year period taking into account the wind generating fleet at the end of 2015.