Real-time Wind Speed Research Articles

Wind speed sensor data processing based on Kaman Vortex Street principle

Wind speed is one of the important monitoring parameters to ensure mine safety. The commonly used mine wind speed sensor can be divided into ultrasonic type, impeller type and hot wire type according to the design principle. The accuracy of the latter two methods is not high enough because they are susceptible to steam and coal dust in mines. The ultrasonic wind speed sensor has better measuring accuracy. In this paper, ultrasonic wind speed sensor, ultrasonic sending and receiving circuit combined with ADC sampling function of the chip, to obtain the received ultrasonic amplitude change data, statistics of ultrasonic waveform changes caused by blowing, according to the number of changes in the wind speed frequency, and then calculate the real-time wind speed.

A novel reinforced online model selection using Q-learning technique for wind speed prediction

An accurate wind speed prediction is a fundamental prerequisite for enhanced wind energy integration with grid. Existing forecasting models are trained on the wind speed data. And these models rely on global accuracy without considering local variation of wind. Due to the local variation of wind, performance of each model varies for every time-step. As a result, this paper implements a robust and novel hybrid framework i.e. online model selection using Q-learning (OMS-QL) provided forecasting model pool (FMP). The proposed framework is the first model developed for online selection of best forecasting model dynamically using reinforcement learning (RL) approach. The proposed framework is mainly clustered into two parts: FMP, and Q-learning agent for online model selection. First, FMP is constructed using nine robust approaches, which are trained on the wind speed data. Then, Q-learning agent is developed to dynamically select the best prediction approach at every time-step for improved accuracy. Two experiments are conducted using the real-time wind speed datasets. Experimental results indicate that the proposed OMS-QL framework improved by 47% and 48% in both case studies compared to benchmark models.

Social Welfare Maximization of Competitive Congested Power Market Considering Wind Farm and Pumped Hydroelectric Storage System

The utilization of wind energy sources with energy storage systems has been increased in the power sector to satisfy the consumer’s energy demand with minimum price. This paper presents the impact of a wind farm (WF) and pumped hydroelectric storage (PHS) system in the competitive electricity market under a congested transmission system. The PHS system is used to compensate for the deviation of WF generation in the real-time electricity market. To investigate the impact of the proposed method, initially, the market-clearing power problem is solved without consideration of WF and PHS systems, and again it is solved with the WF and PHS systems. The optimal location of the WF and PHS systems is decided by the bus sensitivity factor (BSF) of these systems. The analysis is carried out by using generator sensitivity factor (GSF) with the help of the moth flame optimization (MFO) algorithm and thereby calculating market clearing price (MCP) and market clearing volume (MCV). The MFO algorithm is used here for the first time for solving the congested market-clearing power problem with the integration of WF and PHS systems under deregulated environment. The presented approach shows the improvement of social welfare after the placement of WF and PHS in the congested deregulated system. Modified IEEE 30 bus system is used to solve the market-clearing power problem and results obtained from the MFO algorithm are compared with the firefly algorithm (FA). Three different real-time wind speed data have been considered here to verify the proposed approach with uncertainty and the continuously changing nature of wind flow. It is discovered that social welfare is improved with the quantity addition of wind power, regardless of optimization techniques.

A novel hybrid framework for wind speed forecasting using autoencoder‐based convolutional long short‐term memory network

A precise forecast of wind speed is a fundamental requirement of wind power integration. The nonlinear and intermittent nature of the wind makes wind speed forecasting (WSF) complicated for linear approaches. Addressing the complications faced by the linear approaches, this paper proposed a novel and robust approach using long short-term memory (LSTM) autoencoder, convolutional neural network (CNN), and LSTM model for enhanced WSF. The proposed hybrid approach is divided into two main components: feature encoding, dimensionality reduction using LSTM autoencoder and forecasting using convolutional LSTM. In the first stage, the LSTM autoencoder eliminates the uncertainties present in raw wind speed data and also reduces the computational load on the forecasting convolutional LSTM approach. Then, in the second stage, CNN is used to extract the optimum features, and the LSTM network is used to forecast the wind speed. Five different benchmark forecasting models are used to evaluate and study the proposed hybrid approach's performance. The experiment is performed with real-time wind speed data from the Garden city wind farm, USA. The proposed hybrid approach performance is verified using various performance metrics. The experimental results demonstrate that the proposed approach improved by 40% over the second best benchmark forecasting approach.

Wind farm integrated restructured electricity market analysis using Ant Lion optimizer algorithm

ABSTRACT The optimal market strategy investigation is an enormous task for the system operator in a wind-integrated restructured power system. Bidding makes competition among all the market players in an electricity market and it forces to operate transmission lines near to their thermal limit and as an outcome, congestion may occur in the transmission system. The wind farm can take a vital role in this situation to mitigate transmission congestion by providing some additional powers to the system. To present the imprint of the wind farm in a deregulated power system, the work is formulated in a congested transmission environment considering the maximization of social welfare (SW) as an objective function. A generator shift factor (GSF)-based approach is presented in the work to mitigate the transmission congestion by rescheduling the supplier power. The power transfer distribution factor (PTDF) decides the optimal place of the wind farm (WF) in the system. Ant lion optimizer (ALO) algorithm is used in a modified IEEE 30-bus system to analyze the work. To validate the results obtained from the ALO algorithm, a comparison study is performed in this work with other three meta-heuristic algorithms namely particle swarm optimization (PSO), artificial bee colony (ABC), and moth swarm algorithm (MSA). The comparative results show that the ALO algorithm is much effective than the other three considered algorithms for the presented problem formulations. Four different real-time wind speed data are taken to check the efficiency of the presented approach with considering the intermittent nature of wind speed. It is revealed that the social benefit is raised with increment in wind speed irrespective of optimization techniques.

High-frequency assessment of air and water quality at a concentration animal feeding operation during wastewater application to spray fields

Air and water quality at a concentrated animal feeding operation (CAFO) in Eastern North Carolina that uses a covered lagoon and anaerobic digester was evaluated for 2 weeks in August 2020. Real-time PM2.5 mass concentrations were determined using a reference ADR-1500 nephelometer and high-frequency measurements of dissolved inorganic nitrogen (DIN) were evaluated using autonomously logging sensors. Air and water quality parameters were assessed before, during and after wastewater from the lagoon was irrigated onto adjacent spray fields. Reference measurements were conducted alongside a HOBO weather station to collect real-time wind speed and direction, temperature, and humidity measurements. PM2.5 concentrations varied between 0 and 159 μg/m3 with an average concentration of 11 μg/m3, below EPA standard for secondary aerosols of 15 μg/m3. Higher PM2.5 concentrations were observed when wind originated from swine barns but not from covered lagoons. Water quality data showed that DIN concentrations downgradient from the CAFO were elevated relative to upstream concentrations. A groundwater seep that drains a spray field contained the highest average DIN concentration (31.0 ± 12.8 mg L−1), which was 25 times greater than upstream DIN concentrations (1.2 ± 0.8 mg L−1). Average DIN concentration at the downstream station was lower than the seep concentration (8.6 ± 16.2 mg L−1), but approximately 8 times greater than upstream. Air quality data show that the lagoon cover was effective at mitigating air quality degradation, whereas DIN concentrations in water were similar to previous studies on CAFOs using open lagoons. In addition, air and water quality parameters were significantly (p < 0.001) higher after irrigation, indicating possible influence due to ammonia and nitrate elevation. Additional research is needed to compare high-frequency data collected from swine CAFOs using capped and uncapped lagoon systems to better understand spatiotemporal air and water quality trends of this practice.

Improving Power Harvesting Ability of Variable Speed Wind Turbine using Intelligent Soft Computing Technique

This paper presents a study carried out on maximizing energy harvesting of wind turbines. One way of improving the output power of the wind turbines is by optimizing the power conversion coefficient. The power conversion coefficient factor is expressed as a function of the wind turbine blade tip speed ratio and the turbine blade pitch angle. Optimization of the wind turbine generator output power is done by considering the effects of variations of wind speed, blade tip speed ratio, and pitch angle. An intelligent soft computing technique known as an adaptive neuro-fuzzy inference system (ANFIS) with a fuzzy logic controller for blade pitch actuator was applied to optimize the generator output power. The simulation result showed that the power conversion coefficient of 0.513 is achieved. The study was verified by using real-time wind speed data of Adama II wind farm in Ethiopia and specifications of the Gamesa G80 horizontal axis wind turbine generator unit by MATLAB software. Accordingly, a promising and satisfying improvement in power harvesting capacity is obtained. The output power of this generator is improved by 9.47% which is by far better result as compared to the existing literature.

Multi-step real-time wind speed prediction based on convolution memory network

Multi-step real-time prediction based on the spatial correlation of wind speed is a research hotspot for large-scale wind power grid integration, and this paper proposes a multi-location multi-step wind speed combination prediction method based on the spatial correlation of wind speed. The correlation coefficients were determined by gray relational analysis for each turbine in the wind farm. Based on this, timing-control spatial association optimization is used for optimization and scheduling, obtaining spatial information on the typical turbine and its neighborhood information. This spatial information is reconstructed to improve the efficiency of spatial feature extraction. The reconstructed spatio-temporal information is input into a convolutional neural network with memory cells. Spatial feature extraction and multi-step real-time prediction are carried out, avoiding the problem of missing information affecting prediction accuracy. The method is innovative in terms of both efficiency and accuracy, and the prediction accuracy and generalization ability of the proposed method is verified by predicting wind speed and wind power for different wind farms.

WLEACH: Real-Time Meteorological Data Based Wind LEACH

Introduction:Nowadays, Wireless Sensor Network (WSN) plays an important role in various fields. The limited power capability of the sensor nodes in the WSN brings constraints on the performance of the network. Low Energy Adaptive Clustering Hierarchy (LEACH) is a promising protocol for WSN that suffers from higher energy consumption.Objective:The primary objective of this study is to give an alternate harvesting resource power to sensor nodes in the LEACH algorithm which can be equally capable of providing the same or sometimes better results.Methods:This study is based on real-time meteorological data. A real-time wind speed data is taken for the starting of a day to the end of the day on an hourly basis from the weather forecast. Now to convert this rotational energy into electrical energy, we used two types of wind turbines. For the proposed methodology, a micro wind turbine generator and 300watt wind turbine are used. Then this converted electrical energy is given to sensor nodes. For the clustering, the wind power operated nodes are given maximum preference to be elected as the cluster heads based on realtime wind meteorological data. We consider 10 wind-powered sensor nodes. As we increase the number of wind-powered sensor nodes in the network, the performance is increased in terms of a lifetime but then increases the complexity of the network. These wind-powered nodes remain alive in the network. Since the deployment of the sensor nodes is random, each simulation runs for 5 times and the average of first node dead, half node dead and last node dead is considered.Results:The experimental results for the micro wind turbine generator are compared based on with and without the MPPT controller. MPPT controller gives the maximum power by using the tip speed ratio control, power signal feedback control, and hill climb search control method. Therefore, the network lifetime should be higher for the MPPT based wind generator. Network lifetime and Energy consumption are compared for a micro wind turbine generator and 300watt wind turbine. Finally, the performance of the proposed system is compared with the modified solar LEACH implemented using real-time meteorological data.Conclusion:This paper has investigated the wind-based LEACH which uses the real-time meteorological data for the selection of the cluster head. Two types of wind generators are considered for the implementation and it is found that the performance of the commercial 300W wind turbine and the micro wind turbine with and without MPPT is almost similar since the data from both wind turbines are given on hourly basis. The performance of the wLEACH is compared with the sLEACH which shows that the network lifespan of the wLEACH is also nearly the same compared to the sLEACH. However, it was found that wind power generation is cheaper and efficient than solar power generation. Therefore, it is inferred that this proposed wLEACH provides a costefficient solution.

Impact of wind power generation on ATC calculation with uncertain equal load

Available transfer capability (ATC) is very important for system operators for a fair and transparent electricity market. There are many methods to calculate and to improve the ATC value in the power system. Due to green energy focus across the globe, renewable energy integration to the system to improve the ATC value need to be established. Lots of literature have been published on wind power integration to improve ATC value using active power support. During highly loaded conditions, reactive power plays an important role. In this paper, active as well as reactive power of wind turbine generation for ATC calculation has been considered. The reactive power limit has been considered based on the capability curve of the wind turbine generator. Real-time wind speed data for wind power output calculation are taken. The impact analysis of wind power generation on ATC variation under normal as well as contingency cases has been carried out. The effectiveness of the proposed method is tested on the IEEE-30 bus system.

Strategic planning of renewable distributed generation in radial distribution system using advanced MOPSO method

The integration of wind turbine, solar photovoltaic based distributed generation in low voltage distribution network has many advantages. However, the output power from wind turbine and solar PVs are intermittent in nature. The production of these intermittent power generations may introduce many challenges in distribution network such as increase in power losses, voltage deviation and instability. Therefore, this paper proposes optimal integration of these intermittent renewable DGs along with biomass and capacitor banks to optimize distribution network parameters. The real time wind speed and solar irradiance data are processed in time series mathematical model along with relevant probability distribution function (PDF) for calculating the accurate power output from these renewable resources. Later, an advanced multi-objective particle swarm optimization technique has been used to optimize the distribution network parameters such as power losses, voltage deviation and voltage stability index. The proposed model has been validates and tested on Portuguese 94 bus system. Moreover, the proposed model results revealed the power loss reduction up to 77.82%, voltage deviation 9.68% and voltage stability index 44.25%.

Research on mppt control method of doubly-fed wind power system based on sliding mode control

In order to solve the problem of maximum wind energy tracking wind speed measurement of wind turbine at present, a new wind energy control algorithm, sliding mode control algorithm, is proposed, which adjusts the speed of fan by sliding mode control, avoids the problem of real-time wind speed measurement in wind speed tracking control, and can respond quickly to the sudden change of wind speed and solve the problem of uncertain inherent parameters of wind speed. The proposed algorithm is compared with the PI control algorithm and the analog 660KW generator, and the effectiveness of the algorithm is verified. Results: the sliding mode control algorithm has good track tracking performance, can shorten the delay time between the actual value and the reference value, and has good performance.

A Broad Learning System with Ensemble and Classification Methods for Multi-step-ahead Wind Speed Prediction

Short-term wind speed prediction plays a significant role in the management of large-scale wind power plants. However, wind speed prediction is extremely complex and difficult due to the volatility and non-linearity of wind. For this purpose, a broad learning system (BLS) with ensemble and classification named BLS-EC is proposed to predict multi-step-ahead wind speed. The proposed method is based on a new neural network termed the BLS, which could work out the complex non-linear relation by learning model while ensuring the computational efficiency. To overcome the randomness and instability of a single BLS, this paper proposes the BLS ensemble method to improve the generalization and stability of the network. In order to improve the accuracy of prediction, a method called classification-guided regression is proposed to distinguish different variation patterns of initial predicted wind speed. According to the classification result, different pattern sequences are re-predicted to obtain the final prediction result. Applying this thinking and method into research of three real-time wind speed datasets which were taken from Sotavento Galicia SA (SG), Alberta (ALB), and Newfoundland (NFL), the validity and practical value of this method can be demonstrated. Results obtained clearly show that BLS is better than existing methods ARIMA and RBF. Moreover, the BLS-EC method improved generalization performance and the predicting precision of a single BLS. In this study, the BLS-EC was proposed and successfully applied to wind speed prediction.

Power smoothing method of PMSG based grid integrated wind energy conversion system using BESS/DSTATCOM

&lt;p&gt;The output of the PMSG based wind energy conversion system (WECS) is fluctuating in nature due to intermittency of wind speed. The distribution static synchronous compensator (DSTATCOM) incorporated with the battery energy storage system (BESS) is used to smooth the power produced from wind generator system. The control strategy of BESS/DSTATCOM and its integration to mitigate the power fluctuations of grid connected WECS is presented. Three-leg three-phase voltage source converter (VSC) based DSTATCOM is used and the battery current is controlled to smooth the net power injected to the utility grid from wind power generation system. The control strategy implemented has the capability of supplying the required amount of power to the utility with help of batteries. The PQ control strategy is employed to control the three-phase inverter for managing power exchange with the utility grid. The real time wind speed data is considered for the simulation study of the system. The effectiveness of the control strategy of the system is validated through the simulation results in MATLAB/Simulink environment.&lt;/p&gt;

A new prediction method based on VMD-PRBF-ARMA-E model considering wind speed characteristic

With the characteristics of randomness, fluctuation, nonlinearity and uncertainty, wind speed affects the stability of wind power system. In order to improve the safety and stability of wind power system, accurate and effective wind speed prediction is essential. In the paper, a novel wind speed prediction method based on wind speed characteristics is proposed. Firstly, VMD is used to decompose wind speed into the nonlinear part, the linear part and the noise part. Nonlinear part reflects the nonlinear characteristic of wind speed, linear part embodies the linear process of wind speed formation, noise part is the error (ER) sequence decomposed wind speed by VMD. According to the characteristics of different parts, different models are built, PCA-RBF model is built for the nonlinear part, ARMA model under the MCMC framework is built for the linear part, and probability distribution is fitted for the noise part. These three parts are combined to establish VMD-PRBF-ARMA-E model to make off-line deterministic prediction and uncertainty prediction. Then the superiority of VMD-PRBF-ARMA-E model is verified by comparing with other nonlinear models and time series models. At last, based on off-line scheme, VMD-PRBF-ARMA-E model is used to make real-time wind speed prediction. The deterministic prediction of VMD-PRBF-ARMA-E model has high accuracy, and can reflect the characteristics of wind speed well and truly, which can provide a scientific basis for the power grid dispatching department, and help to ensure the stability of wind power system.

A flutter-effect-based triboelectric nanogenerator for breeze energy collection from arbitrary directions and self-powered wind speed sensor

Triboelectric nanogenerators (TENGs) have been developed rapidly into an efficient wind energy collection equipment. Reducing the friction wear and energy loss in breeze energy collection is a research direction worthy of attention. Herein, a flutter-effect-based triboelectric nanogenerator (FE-TENG) is designed to collect the breeze energy at low wind speed from arbitrary directions. Distinguishing from previous wind-driven TENGs, the wind-driven part of this device is separated from the TENG units, which not only avoids the wear of friction layers caused by direct wind contact but also reduces the energy loss, therefore, relatively stable electric outputs are obtained with VOC ~ 281 V, ISC ~ 13.4 μA, QSC ~ 143 nC, and output power ~ 4 mW at the wind speed of 4.5 m/s, respectively. In addition, a real-time wind speed monitoring system based on LabVIEW software with high sensitivity and fast response to wind is achieved relying on the excellent linear relationship between wind speed and electrical output signal. Furthermore, it has been successfully applied as power sources for portable electronics, about 170 commercial light-emitting devices (LEDs) are lighted and a digital watch is successfully driven at the wind speed of 2.9 m/s. This work not only provides a new structure and idea for the future collection of clean and sustainable breeze energy from arbitrary directions but also has great potential in the field of self-powered systems.

Analysis of the Influence of Water-Vapor Correction Term on the Measurement Uncertainty of Wind Speed

In order to explore the influence of water-vapor correction term on wind speed uncertainty, the actual measured value of wind speed by pitot tube is the research object, Firstly, the uncertainty value of the wind speed value obtained by the simplified model (discarding the water-vapor correction term) is evaluated by GUM method. Then, the MCM evaluates the uncertainty of the wind speed value obtained from the original model (including the water-vapor correction term). Finally, two kinds of the evaluation results obtained by the method are compared in the form of probability distribution graphics, and the numerical analysis is carried out. After the water-vapor correction item is discarded, the fluctuation degree and dispersion degree of the measurement result will be reduced, and the degree of reduction is 1.0988% and 1.0929%, respectively. The degree of volatility is about 25.8513% of the degree of dispersion. The degree of influence on the volatility and dispersion of the measurement result is about 0.2305% and 0.8987% of the measured value of wind speed. In the application, for the occasion of pursuing the accuracy of measured value of wind speed, it is recommended to use the original model to measure the real-time wind speed. For the occasion of the stability of the wind speed measurement result, the GUM method can be used to evaluate the uncertainty of the simplified model.

Discovering time-varying aerodynamics of a prototype bridge by sparse identification of nonlinear dynamical systems.

Vortex-induced vibrations (VIVs) have been observed on a long-span suspension bridge. The nonstationary wind in the field characterized by the time-varying mean wind speed is likely to lead to time-varying aerodynamics of the wind-bridge system during VIVs, which is different from VIVs induced by stationary or even steady wind in wind tunnels. In this paper, data-driven methods are proposed to reveal the time-varying aerodynamics of the wind-bridge system during VIV events based on field measurements on a long-span suspension bridge. First, a variant of the sparse identification of nonlinear dynamics algorithm is proposed to identify parsimonious, time-varying aerodynamical systems that capture VIV events of the bridge. Thus we are able to posit new, data-driven, and interpretable models highlighting the aeroelastic interactions between the wind and bridge. Second, a density-based clustering algorithm is applied to discovering the potential modes of dynamics during VIV events. As a result, the time-dependent model is obtained to reveal the evolution of the aerodynamics of the wind-bridge system over time during an entire VIV event. It is found that the level of self-excited effects of the wind-bridge system is significantly time varying with the real-time wind speed and bridge motion state. The simulations of VIVs by the obtained time-dependent models show high accuracies of the models with an averaged normalized mean-square error of 0.0023. The clustering of obtained models shows underlying distinct dynamical regimes of the wind-bridge system, which are distinguished by the level of self-excited effects.

A power output and mechanical model of a wind turbine and its control strategy under severe wind conditions

Under the current wind turbine control strategy, wind turbines are cut off to guarantee their safety for wind speeds exceeding 25 m/s. However, under severe wind conditions, power grids with high wind power penetration will suffer a catastrophic consequence due to the large-scale cut-off of wind turbines. To solve the problem, mechanical effects on a wind turbine from the aspect of airflow are analyzed in this paper; a numerical connection between the thrust coefficient and the wind power coefficient is proposed. On the basis of those analyses, the accumulated load on the blades and the tower of the wind turbine are analyzed, and a cohesive control strategy is proposed. The strategy uses real-time wind speed data to calculate the mechanical load on a wind turbine and realizes the maximum active power output under severe wind conditions. The results of case studies verify the effectiveness and superiority of the proposed strategy.

Human Comfort Instrument Design Based on Embedded

The traditional human comfort meter has the following defects: the interface is not uniform; the operation is cumbersome and complicated; the interface is unfriendly, and the stability and adaptability are poor. This paper presents a design scheme for human comfort instrument based on embedded system, using S3C2440 embedded development board and the sensors to collect the real-time temperature, relative humidity and wind speed data and to process the collecting data; then obtaining the human body comfort value according to the basic algorithm of human body comfort instrument; giving the human comfort conclusion according to the diastolic index range of human comfort, and showing the temperature and humidity, wind speed, comfort value and conclusion through writing the Qt graphical user interface program. At the same time, the human comfort instrument has the data storage function. The human comfort instrument is high in integration, strong in real time, high in sensitivity, stable and reliable, and it meets the development goals of the intelligent meteorological service, and meets the demand of the meteorological service that is closer to life, and it has broad development prospect.