Turbine Location Wind Speed Forecast Using Convolutional Neural Network

In the traditional wind speed forecast of a general wind farm, this farm is often regarded as a whole for point forecasting, and only a single wind speed forecast result is given in a large spatial scale. However, wind turbines are distributed in diverse locations in the wind farm, and wind speeds are often different. Therefore, this paper comes up with a model to forecast the wind speeds of multiple wind turbines by learning the potential relationship between wind speed time series and wind direction time series. First, wind direction feature is preprocessed by trigonometric function. Subsequently Simple Recurrent Unit (SRU) is designed to learn the temporal correlation information. Wind speed and trigonometric wind direction form a three-dimensional feature matrix. Compared with other Recurrent Neural Network (RNN), SRU can greatly reduce the computational cost. Therefore, SRU is used to extract the dynamic information of characteristic data of multiple wind turbines in a wind farm over time. The simulation results with the real data prove that compared with the existing forecast methods using temporal correlation feature, the method not only significantly reduces the computational cost, but also improves the accuracy of forecast.

Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models

The purpose of micrositing is to find an optimal layout of a group of wind turbines in order to extract maximum power production from a wind farm. In the case of wind farm design, the wake interactions between wind turbines are one of the most critical subjects that should be considered. Because, not only they cause a decrease in wind speed which causes less energy production but also they lead to blade damages on wind turbines and high maintenance costs. Offering high quality layout solutions that needs to be decided before the design of a wind farm will lead to high profits for wind farm investors. Providing options to the investors regarding the quantity and optimal locations of wind turbines is the main concern of this paper, since erecting more turbines in certain locations sometimes may cause energy losses. In this study, a series of latitude-longitude data was generated by scanning the digital map of the wind farm site. The determination of locations where turbines can be placed is presented as a new approach in terms of wind farm area characterization. By doing so, a continuous search space is generated that brings more flexibility to mobilize wind turbines. The solution starts with a heuristic approach, and then a genetic algorithm is followed to find optimal placements of wind turbines considering minimizing the wake loss. At last, the optimum locations of the wind turbines are obtained, and the maximum number of turbines is recommended for the given wind farm.

Researching on interaction between wind farm and power system, it usually uses simple model to describe the wind farm. The wind turbines in a wind farm always suppose as a coherent generator group, every wind turbines have identical wind speed. As wind turbine locates in the different site in the wind farm, the wind speed at each wind turbines will not change simultaneously. Wake effect is another factor that should take into consideration. So, it needs to get wind speed equivalent model. The equivalent model of wind speed in the wind farm is based on output of power. Gust wind and ramp wind are discussed. Wind farm will change gust wind into step varying wind and make ramp wind into different type wind. The arrangement of wind turbines will also affect equivalent model of wind speed. The result show that the length of wind farm will smooth the power output. It explains why the power output fluctuation of wind farm is smaller than single wind turbine. The onshore wind farm located in Dongtai Jiangsu province is proposed as an example. The equivalent wind speed model and equivalent impedance connected to grid are calculated. The results illustrate the impact of wind speed model on wind farm output.

During recent years, wind energy has moved from an emerging technology to a nearly competitive technology. This fact, coupled with an increasing global focus on environmental concern and a political desire of a certain level of diversification in the energy supply, ensures wind energy an important role in the future electricity market. For this challenge to be met in a cost-efficient way, a substantial part of new wind turbine installations is foreseen to be erected in big onshore or offshore wind farms. This fact makes the production, loading and reliability of turbines operating under such conditions of particular interest.

Hybrid wind speed forecasting using ICEEMDAN and transformer model with novel loss function

ARMA based approaches for forecasting the tuple of wind speed and direction

PurposeThe rapid rising of renewable energy sources particularly wind energy cannot be ignored. The numerical increase in wind energy farms throughout the world is the best example. The purpose of this paper is to assess the basic question of whether wind characteristics affect the performance and cost of energy. The importance of this question cannot be ruled out while comparing renewable energy to a conventional form of energy more specifically especially for the developing country where the cost of energy is very high.Design/methodology/approachThe research design of this paper is consists of an assessment of local wind characteristics of the wind farm site using Weibull k and c parameters. The performance model is used to assess the performance of the wind turbine (WT) corresponding to local wind characteristics. The wind correlation with WT in terms of changing wind speed has been assessed to quantify the effects of wind speed on the WT behavior and failure of WT components. Similarly, the power curve of WT is assessed and compared with the International Electrotechnical Commission standards 61400-12-2. The WT power coefficient and tip speed ratio corresponding to wind speed is also investigated. The energy volume and cost of energy lost model is used to determine the cost and volume loss of energy/kWh of the wind farm.FindingsThe findings of practical wind farms showed that the wind conditions of the site are showing a strong tendency that can be determined from the results of Weibull k and c parameters. The k and c parameters are observed to be 3.44 and 9.16 m/s, respectively, for a period of a year. The standard deviation is observed to be 2.56 for a period of a year. WT shows the efficient behavior can be obtained from the power coefficient and tip speed of WT at different wind speeds. Also, wind farm observation showed that to be some increasing wind speed cause of based WT component failures. The results of energy volume and cost/kWh assessment showed that the major portion of energy volume and cost of energy is lost owing to network, voltage dip and frequency surge, electrical and mechanical components failures.Originality/valueGenerally, it can be concluded that the WTs are now able to cope with variable wind speeds. However, the results of this paper are showing that WT performance and availability decreased due to increased wind speeds. It can also be a reason to decreased volume and increase the cost of energy/kWh.

This work introduces a novel method to generate probabilistic hub‐height wind speed forecasts aimed at power output prediction. We employ state‐of‐the‐art convolutional variational autoencoders (CVAEs) trained with historical wind speed observations, multivariable outputs (wind speed, direction, temperature, pressure, and humidity) from a numerical weather prediction (NWP) model and spatio‐temporal encodings. After training, we exploit the CVAE data generating capabilities to produce probabilistic forecasts from the same deterministic dynamical NWP model. The resulting probabilistic forecast provides an insight into the uncertainty of the original deterministic input that compensate for errors due to numerical discretization, inaccuracies in initial/boundary conditions and parameterizations and, most importantly wind speed fluctuations due to complex terrain features. To show the performance of the proposed model, we validate the approach with forecasted and observed data for fifteen sites in a wind farm in Awaji Island, Japan, in a challenging zone with complex topography and highly fluctuating wind patterns. We show that the proposed method provides improved wind speed forecasts from both deterministic (reduced root mean square error) and probabilistic (reduced continuous ranked probability score) standpoints. We also use ensemble forecast validation methods to assess the statistical properties of the CVAE and conclude that in zones with rapidly changing wind speed dynamics, the CVAE ensemble performs in a superior way when compared to the analog ensemble method. Finally, we also provide an insight on how the results can be used to obtain reliable wind power estimations.

The large-scale integration of wind power into the grid has led to a reduction in system inertia, threatening frequency stability. There is an urgent need to accurately assess the inertia support capability of wind turbines, providing a theoretical basis for grid inertia dispatch and supporting grid frequency stability. However, due to factors such as wake effects, time-delay effects, and wind shear effects, the rotational speeds of different wind turbines within a wind farm under certain wind speed conditions exhibit probabilistic distribution characteristics. Existing research on wind turbine inertia assessment rarely accounts for the rotational speed randomness. To address this, this paper proposes a probabilistic assessment method for the available inertia of wind turbines that considers rotational speed randomness, establishes a joint probability model for wind speed and rotational speed, deriving the conditional probability density function of rotational speed. By substituting this into the frequency-domain inertia model, we achieve probabilistic inertia assessment. Using operational data from a wind farm in China, a practical case study is constructed, verifying the accuracy of the proposed probabilistic assessment method. At a wind speed of 6 m/s, the proposed method accurately captures the actual system inertia within its 90% confidence interval, in contrast to a conventional approach which yielded a significant 6.5% error.

An important challenge associated with wind power is the unpredictable nature of the wind. As wind power forecasting is usually based on the relationship between wind speeds and wind power, wind farm data is a critical prerequisite for wind speed prediction, meanwhile it plays an important role on the reliability and safety of the grids. For development of new controls, converter topologies, modulation methods, and maximum power extraction algorithms for wind energy conversion systems, the provision of a controlled test environment showing dynamic and steady state characteristics of wind turbines is desired. In this paper, a small scale wind turbine experimental laboratory model based on separately excited dc-shunt motor and permanent magnet synchronous generator (PMSG) is proposed, which reproduces the static and dynamic characteristics of the wind turbine without any wind environment. A programmable wind speed model is established that can satisfactorily simulate the spatial effect of the wind behavior, including gust change, ramp variations, and noise effect. The dynamics of the simulator is studied using the drive train model of the armature controlled dc shunt motor. A microcontroller along with high frequency buck converter has been used for acceleration and deceleration of the dc motor. It is shown through the frequency response characteristics that the simulator model approximates the wind turbine model dynamics. Simulation and experimental results illustrate that the speed of the motor changes with variation in the applied terminal voltage, like in the real wind turbine where the turbine speed changes according to the wind speed. Simulation results are obtained through PSCAD/EMTDC simulation software and experimental results are obtained through the programmable microcontroller used with the DC-motor-PMSG set.

Wind power is also known as junk. This is because wind power fluctuations affect the security and stability operation. Wind power wind turbines created is mainly concerned with the speed of wind. Because of the wind direction uncertain, intermittent, and wake effects between each unit wind farm, wind turbines cannot make that kind of power according to the demand for energy as conventional generators. Due to the lack of experimental data, assess the volatility of wind power is still a lack of effective methods. This article studies the sample in a northeast wind farm power, and based on a sliding differential algorithm, distribution fitting and quantitative calculations describe the characteristics of wind power fluctuations. This article studies the sample in a northeast wind farm power, and based on a sliding difference algorithm, through the analysis showed that wind power fluctuations obey t location scale distribution. And it is affected by factors such as spatial and temporal distribution, there is a big difference between the output power fluctuation characteristics of wind farm output power and single wind turbine. This is due to the wind turbine suffered varying differences, and wake effects between field units, making the distribution of frequent power fluctuations; relative to a single unit, the fluctuation of the whole wind farm is more gentle, that is to say with the spatial distribution increased scale, wind power fluctuations presents certain "gentle effect."

This paper presents an improved approach to assess wind turbine reliability considering the impact of hub levels using Monte Carlo simulation. The hub level of wind turbine affects wind turbine reliability in two aspects: 1) different hub levels are subject to different wind speeds, which produce different wind power outputs; 2) due to the correlation between wind speeds and turbine failure rates, wind turbines with different hub levels have different rates of failure. Therefore, it is important to consider the wind turbine's height, particular hub levels, when evaluating wind turbine reliability. The purpose of this paper is to develop a model to evaluate wind farm reliability while considering the effect of hub levels on wind turbine output and on wind turbine failure rates. The proposed approach is tested on the IEEE RTS-79 system with relevant modifications using probabilistic simulation. The efficacy of the approach is verified by evaluating reliability indexes of wind farms when the hub levels of wind turbines are taken into account.

A compound structure of ELM based on feature selection and parameter optimization using hybrid backtracking search algorithm for wind speed forecasting

Cluster-based wind turbine maintenance prioritization for a utility-scale wind farm