The problem of acoustic noise is becoming increasingly serious with the growing use of industrial and medical equipment, appliances, and consumer electronics. Active noise control (ANC), based on the principle of superposition, was developed in the early 20th century to help reduce noise. However, ANC is still not widely used owing to the effectiveness of control algorithms, and to the physical and economical constraints of practical applications. In this paper, we briefly introduce some fundamental ANC algorithms and theoretical analyses, and focus on recent advances on signal processing algorithms, implementation techniques, challenges for innovative applications, and open issues for further research and development of ANC systems.

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Recent Advances on Active Noise Control: Open Issues and Innovative Applications

This paper presents a wind plant modeling and optimization tool that enables the maximization of wind plant annual energy production (AEP) using yaw-based wake steering control and layout changes. The tool is an extension of a wake engineering model describing the steady-state effects of yaw on wake velocity profiles and power productions of wind turbines in a wind plant. To make predictions of a wind plant's AEP, necessary extensions of the original wake model include coupling it with a detailed rotor model and a control policy for turbine blade pitch and rotor speed. This enables the prediction of power production with wake effects throughout a range of wind speeds. We use the tool to perform an example optimization study on a wind plant based on the Princess Amalia Wind Park. In this case study, combined optimization of layout and wake steering control increases AEP by 5%. The power gains from wake steering control are highest for region 1.5 inflow wind speeds, and they continue to be present to some extent for the above-rated inflow wind speeds. The results show that layout optimization and wake steering are complementary because significant AEP improvements can be achieved with wake steering in a wind plant layout that is already optimized to reduce wake losses. Copyright © 2016 John Wiley & Sons, Ltd.

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Maximization of the annual energy production of wind power plants by optimization of layout and yaw‐based wake control

An active noise cancellation system that reduces, at a listening position, power of a noise signal radiated from a noise source to the listening position. The system includes an adaptive filter, at least one acoustic actuator and a signal processing device. The adaptive filter receives a reference signal representing the noise signal, and provides a compensation signal. The at least one acoustic actuator radiates the compensation signal to the listening position. The signal processing device evaluates and assesses the stability of the adaptive filter.

Adaptive noise control system

. The objective of this paper is to compare field data from a
scanning lidar mounted on a turbine to control-oriented wind turbine wake
models. The measurements were taken from the turbine nacelle looking
downstream at the turbine wake. This field campaign was used to validate
control-oriented tools used for wind plant control and optimization. The
National Wind Technology Center in Golden, CO, conducted a demonstration of
wake steering on a utility-scale turbine. In this campaign, the turbine was
operated at various yaw misalignment set points, while a lidar mounted on the
nacelle scanned five downstream distances. Primarily, this paper examines
measurements taken at 2.35 diameters downstream of the turbine. The lidar
measurements were combined with turbine data and measurements of the
inflow made by a highly instrumented meteorological mast on-site. This paper
presents a quantitative analysis of the lidar data compared to the
control-oriented wake models used under different atmospheric conditions and
turbine operation. These results show that good agreement is obtained between the
lidar data and the models under these different conditions.

/pdf/analysis-of-control-oriented-wake-modeling-tools-using-lidar-54jrh4h26r.pdf

Analysis of control-oriented wake modeling tools using lidar field results

Wind Farm Layout Optimization (WFLO) can be useful to minimize power losses associated with turbine wakes in wind farms. This work presents a new evolutionary WFLO methodology integrated with a recently developed and successfully validated Gaussian wake model (Bastankhah and Porte-Agel model). Two different parametrizations of the evolutionary methodology are implemented, depending on if a baseline layout is considered or not. The proposed scheme is applied to two real wind farms, Horns Rev I (Denmark) and Princess Amalia (the Netherlands), and two different turbine models, V80-2MW and NREL-5MW. For comparison purposes, these four study cases are also optimized under the traditionally used top-hat wake model (Jensen model). A systematic overestimation of the wake losses by the Jensen model is confirmed herein. This allows it to attain bigger power output increases with respect to the baseline layouts (between 0.72% and 1.91%) compared to the solutions attained through the more realistic Gaussian model (0.24–0.95%). The proposed methodology is shown to outperform other recently developed layout optimization methods. Moreover, the electricity cable length needed to interconnect the turbines decreases up to 28.6% compared to the baseline layouts.

https://www.mdpi.com/1996-1073/11/12/3268/pdf

Realistic Wind Farm Layout Optimization through Genetic Algorithms Using a Gaussian Wake Model

The FXLMS algorithm, which is extensively used in active noise control, exhibits frequency dependent convergence behavior. This leads to degraded performance for time-varying and multiple frequency signals. A new algorithm called the eigenvalue equalization filtered-x least mean squares (EE-FXLMS) has been developed to overcome this limitation without increasing the computational burden of the controller. The algorithm is easily implemented for either single or multichannel control. The magnitude coefficients of the secondary path transfer function estimate are altered while preserving the phase. For a reference signal that has the same magnitude at all frequencies, the secondary path estimate is given a flat response over frequency. For a reference signal that contains tonal components of unequal magnitudes, the magnitude coefficients of the secondary path are adjusted to be the inverse magnitude of the reference tones. Both modifications reduce the variation in the eigenvalues of the filtered-x autocorr...

Eigenvalue equalization filtered-x algorithm for the multichannel active noise control of stationary and nonstationary signals.

A method for reducing multi-modality in the wind farm layout optimization problem

. In this paper, we develop computationally efficient techniques to calculate
statistics used in wind farm optimization with the goal of enabling the use
of higher-fidelity models and larger wind farm optimization problems. We
apply these techniques to maximize the annual energy production (AEP) of a
wind farm by optimizing the position of the individual wind turbines. The AEP
(a statistic) is the expected power produced by the wind farm over a period
of 1 year subject to uncertainties in the wind conditions (wind direction
and wind speed) that are described with empirically determined probability
distributions. To compute the AEP of the wind farm, we use a wake model to
simulate the power at different input conditions composed of wind direction
and wind speed pairs. We use polynomial chaos (PC), an uncertainty
quantification method, to construct a polynomial approximation of the power
over the entire stochastic space and to efficiently (using as few simulations
as possible) compute the expected power (AEP). We explore both regression and
quadrature approaches to compute the PC coefficients. PC based on regression
is significantly more efficient than the rectangle rule (the method most
commonly used to compute the expected power). With PC based on regression, we
have reduced on average by a factor of 5 the number of simulations
required to accurately compute the AEP when compared to the rectangle rule
for the different wind farm layouts considered. In the wind farm layout
optimization problem, each optimization step requires an AEP computation.
Thus, the ability to compute the AEP accurately with fewer simulations is
beneficial as it reduces the cost to perform an optimization, which enables
the use of more computationally expensive higher-fidelity models or the
consideration of larger or multiple wind farm optimization problems. We
perform a large suite of gradient-based optimizations to compare the optimal
layouts obtained when computing the AEP with polynomial chaos based on
regression and the rectangle rule. We consider three different starting
layouts (Grid, Amalia, Random) and find that the optimization has many local
optima and is sensitive to the starting layout of the turbines. We observe
that starting from a good layout (Grid, Amalia) will, in general, find better
optima than starting from a bad layout (Random) independent of the method
used to compute the AEP. For both PC based on regression and the rectangle
rule, we consider both a coarse ( ∼225 ) and a fine
( ∼625 ) number of simulations to compute the AEP. We find that for
roughly one-third of the computational cost, the optimizations with the
coarse PC based on regression result in optimized layouts that
produce comparable AEP to the optimized layouts found with the fine
rectangle rule. Furthermore, for the same computational cost, for the
different cases considered, polynomial chaos finds optimal layouts with
0.4 % higher AEP on average than those found with the rectangle rule.

/pdf/polynomial-chaos-to-efficiently-compute-the-annual-energy-3g38n35zt2.pdf

Polynomial chaos to efficiently compute the annual energy production in wind farm layout optimization

The active control of tractor noise requires the ability to track and control a signal that changes in frequency as the speed of the engine, in revolutions per minute (rpm), changes during operation. The most common control approach is typically based on some version of the filtered-x algorithm. For this algorithm, the convergence and tracking speed are functions of the frequency dependent eigenvalues of the filtered-x autocorrelation matrix. To maintain stability, the system must be implemented based on the slowest converging frequency that will be encountered. This often leads to significant degradation in the overall performance of the control system. This paper will present an approach which largely overcomes this frequency dependent performance, maintains a relatively simple control implementation, and improves the overall performance of the control system. The control approach is called the eigenvalue equalization filtered-x least mean squares (EE-FXLMS) algorithm and its effectiveness will be demonstrated through an application to tractor noise in a mock cab. Experimental results will be presented which show that the EE-FXLMS algorithm has faster convergence times and provides on average a 1 dB increase in attenuation. A 3.5 dB increase in attenuation was seen in some of the cases presented.

Eigenvalue equalization filtered-x ( EE-FXLMS) algorithm applied to the active minimization of tractor noise in a mock cabin

/pdf/gradient-based-optimization-of-wind-farms-with-different-xy2sak78eh.pdf

Jared Thomas

Papers

Eigenvalue equalization filtered-x algorithm for the multichannel active noise control of stationary and nonstationary signals.

A method for reducing multi-modality in the wind farm layout optimization problem

Polynomial chaos to efficiently compute the annual energy production in wind farm layout optimization

Eigenvalue equalization filtered-x ( EE-FXLMS) algorithm applied to the active minimization of tractor noise in a mock cabin

Gradient-Based Optimization of Wind Farms with Different Turbine Heights