Are there options for measuring immission levels instead of relying on cumbersome emission calculations?
The wind industry frequently proposes so-called "silent modes," which adjust the pitch of the blades when higher wind speeds occur. However, no one—not even the environmental agencies—verifies whether these modes are actually active. The fact that this actually generates increased turbulence behind the blades goes unnoticed. Furthermore, turbine manufacturers appear to offer capabilities for internal monitoring and alternative SCADA control systems, yet no information about these is published. However, the download below reveals that off-the-shelf methods exist to significantly reduce noise output.
Another "purple elephant in the room" is the alleged necessity of basing calculations on a specific wind turbine emission source strength—incorrectly modeled as a point source. Propagation to a location such as a home is then calculated using highly outdated science and methods (ISO-9213, Geomilieu); these apply a hemispherical model, whereas wind turbines actually begin dispersing sound as a full, undamped sphere. A year’s worth of production data is converted—via wind speed correlations—into source frequency strengths that are assumed to be uniform in all directions and independent of other meteorological data. A case in point is that a blade positioned perpendicular to the wind can easily project sound 100 meters further toward a nearby home. This method is baffling; one might almost suspect it is chosen specifically to avoid transparently sharing knowledge and clarity. The potential for manipulation often breeds mistrust, and the method itself is now scientifically obsolete.
Modern computers make it easy to analyze noise data. This allows background noise to be filtered out of immission measurements taken at the facades of homes. It is almost inconceivable nowadays, but a practice that is already standard and mandatory for construction work has been completely ignored in the realm of wind turbine noise. Then again, wind turbines are the only type of facility exempted from general noise regulations for industry.
Introduction
Currently, following the lead of the wind energy industry, our government maintains that noise immission from a wind turbine at a residential property cannot be measured directly—in fact, it is prohibited. The stated reason is the presence of background noise and the fact that wind direction and speed vary significantly. Yet, how simple it would be to record data at a nearby home using a high-quality, continuously monitoring microphone (capable of capturing frequencies below 1 Hz without attenuation) and then analyze that sound data on a modern computer. Such methods are already employed at active construction sites.
The central research question is how the specific sound of a wind turbine at a residential location can be directly detected and measured without shutting down the turbine.
If background noise can be successfully isolated with sufficient accuracy and verifiability, a new opportunity arises to determine the actual noise contribution of wind turbines at residential properties. This would be particularly valuable in situations where background noise fluctuates significantly and current measurement and calculation methods fail to provide sufficient clarity regarding the turbine's own contribution.
A renowned acoustic engineering firm began working on this several years ago, yet the results have not been shared. Is this because the wind industry—as a client to such firms—prefers to maintain the status quo of manipulating sound propagation data?
The most significant development is the increasing ability not only to measure total noise levels but also to determine the sound's origin and identify the specific source responsible for it. For a measurement method applicable to residential settings, a combination of a small microphone array, data on turbine rotational speed and power output, analysis of periodic sound fluctuations, and a background noise model appears particularly promising. The aim is to establish a setup capable of long-term monitoring under varying weather conditions and turbine operating states.
1. The problem: what sound comes from the wind turbine?
When sound is measured at a home, it almost always consists of a mix of different sound sources. These include the sound of a wind turbine, but also traffic, agricultural machinery, aircraft, birds, insects, rustling trees, and the wind itself.
The major problem is that a sound meter aggregates all these sounds. The meter does not distinguish between the various sources. If, for instance, the sound level at a home is 48 dB(A), it is not immediately clear what portion is caused by a wind turbine and what portion constitutes so-called background noise.
A common way to determine turbine noise is to measure the total sound and subtract the background noise. That sounds simple, but in practice, it is difficult. Both turbine noise and background noise depend heavily on weather conditions; wind direction, wind speed, turbulence, and atmospheric stability all play a role.
Consequently, a background noise measurement taken at a different time is not automatically representative of the conditions present during the wind turbine measurement.
The key question, therefore, is: can we directly identify and separately measure the sound of a wind turbine while the turbine continues to operate?
2. Existing and new measurement methods
Measuring with the wind turbine on and off
The simplest method is to first measure the total sound while the wind turbine is running, and then measure the background noise once the turbine has been stopped. By comparing the two measurements, the turbine's contribution can be calculated. This method is only reliable if weather conditions during both measurements are sufficiently comparable. That is not always the case. Moreover, shutting down a turbine for measurements is practically difficult, especially when long-term monitoring is required.
Long-term measurement and statistical comparison
A more promising method involves continuously measuring noise over a period of weeks or months and combining this with data on wind conditions and turbine operation. This process records not only noise levels but also parameters such as wind speed, wind direction, temperature, electrical power output, and rotor speed. Data regarding the pitch of the rotor blades can also be utilized.
By comparing all this data, it is possible to build a picture of how the noise changes as the turbine’s operating mode or weather conditions shift. The advantage here is that there is no need to designate a single, isolated measurement as the "background noise." Instead, a large volume of measurement data is used to statistically distinguish between turbine noise and other ambient noise.
Recognizing the characteristic sound
Wind turbines have a distinct advantage when it comes to identifying their own noise: the rotor blades rotate at a specific speed. With every revolution, the blades repeatedly pass the tower and move through the air. This gives the sound a periodic character. For instance, the sound intensity may rise and fall slightly in rhythm with the rotating blades—a phenomenon known as amplitude modulation.
By comparing the recorded noise with the turbine's actual rotational speed, one can determine whether these periodic fluctuations align. This can yield a kind of acoustic fingerprint. This type of recognition is particularly interesting because it considers not only the pitch of the sound but also how the sound behaves over time.
Linking the sound to turbine operation
A further step involves comparing the sound with independent data on the turbine, such as rotational speed, power output, and potentially the pitch of the rotor blades. Specialized signal processing allows for the investigation of whether specific components of the recorded sound are linked to the turbine's operation; this is known as coherence analysis. If a sound component shows a clear correlation with the turbine's rotational speed or blade-passing frequency, it indicates that the sound component actually originates from the turbine. This method can help distinguish turbine noise from, for instance, traffic, agricultural machinery, or other sound sources that happen to be present at the same time.
3. Determining the sound source using multiple microphones
Two microphones
A relatively simple extension of standard sound measurement involves placing two microphones. One microphone can serve as a reference, while the other measures the sound at the residence. Because the microphones are located at different positions, they pick up the sound with slight differences in intensity and arrival time. By analyzing these differences, it is possible to determine which sounds are correlated at both locations and which are primarily local. This yields more information than simply recording two separate sound levels. However, the setup must be chosen carefully to ensure the two microphones provide genuinely useful information regarding the same sound source.
A microphone array
A more advanced solution involves using a group of, for example, four to eight microphones that function as a single measurement system. Such a setup is known as a microphone array. Sound reaches each microphone at a slightly different time and with a slightly different intensity. By analyzing these differences, the direction from which the sound originates can be determined; this process is called beamforming. This allows the system to, in effect, "listen" selectively in a specific direction—such as towards a wind turbine.
Research using larger microphone arrays has already demonstrated that it is possible to spatially distinguish between various sound sources on or around a wind turbine. For instance, sounds generated by the rotor blades can be distinguished from mechanical noises originating in the nacelle. For measurements at residential locations, a smaller array might provide sufficient information to determine whether a significant portion of the sound is coming from the direction of the wind turbine. The combination of direction and sound pattern
The most interesting possibility arises when the direction from which the sound originates is combined with the turbine's characteristic sound pattern and its actual rotational speed.
A sound coming from the direction of the turbine that varies at the correct rhythm and correlates with the rotational speed is far more likely to be identified as turbine noise than a sound defined merely by a specific sound level. This makes it increasingly difficult to confuse turbine noise with other sources in the surrounding area.
4. Modern signal processing and artificial intelligence
In addition to using multiple microphones, there are various techniques for digitally separating mixed sounds. This process is known as Blind Source Separation. It involves a computer attempting to reconstruct individual sound sources from a combined recording.
Various mathematical techniques exist for this purpose. Some search for independent sound sources, while others identify recurring patterns in the sound spectrum. In theory, this allows a recognizable wind turbine pattern to be isolated, even when other sound sources are present.
Artificial intelligence can also play a role here. A computer can be trained to recognize the presence of wind turbine noise based on recordings and characteristics such as frequencies, sound intensity, and periodic variations. Techniques such as neural networks and other classification methods can be employed for this purpose.
However, for a method used in official noise measurements or enforcement, it is crucial that the results are verifiable and explainable. A computer system that merely indicates the probable presence of turbine noise, without providing insight into the underlying cause, is less suitable as evidence. Therefore, it makes sense to combine artificial intelligence primarily with physically explainable measurement methods, such as directional analysis, rotational speed analysis, and the detection of periodic sound fluctuations.
5. Improving background noise prediction
Another option is to not only detect turbine noise but also predict background noise more accurately. To this end, a long-term database can be compiled covering periods when turbine noise is known or can be considered negligible.
Subsequently, one can analyze the background noise levels typically expected under specific weather conditions—such as particular wind speeds, wind directions, and atmospheric stability. When the turbine is operating, the measured noise can be compared against this expected background noise. This represents an improvement over relying on a single, arbitrary background measurement, as the comparison accounts for the conditions that influence background noise.
An additional technique involves using a second reference microphone positioned to capture primarily local background noise while minimizing the pickup of turbine noise. If the background noise levels at both microphones are sufficiently similar, a computer can attempt to estimate the background noise at the measurement location and filter it out.
While this technique is already used in other areas of acoustics, it still requires experimental investigation and validation for official wind turbine noise measurements.
6. A combined measurement method
The most promising approach is to combine various techniques rather than relying on a single measurement method. A possible measurement setup at a residence involves a small microphone array linked to data on the wind turbine's operation and meteorological conditions. The microphones determine the direction from which the sound originates. The sound spectrum is then analyzed for periodic changes corresponding to the rotor blade rotation speed. Simultaneously, the sound is checked for correlations with the turbine's power output and other operational data.
Additionally, a model can be used to estimate the expected background noise under the prevailing weather conditions. Combining this information yields a much more robust estimate of the actual turbine noise at the residence.
The goal is not merely to determine the total ambient noise level, but specifically to identify the portion demonstrably caused by the wind turbine.
7. Relationship with Dutch regulations
In the Netherlands, wind turbine noise is determined in accordance with the Environment Regulation using a prescribed measurement and calculation method. This method is based on the noise produced by the turbine itself, which varies according to wind speed. A calculation is then performed to determine how this noise propagates into the surrounding area.
The international standard IEC TS 61400-11-2:2024 focuses specifically on measuring wind turbine noise at the location where it is perceived by people—for example, at a residence.
This approach aligns better with the actual noise exposure at the receptor location than simply measuring noise emissions at the turbine and then theoretically calculating propagation. Under certain circumstances, Dutch regulations allow for the use of a different measurement method, provided it is equally or more accurate and its application is stipulated in the decision.
A new method that isolates turbine noise directly from the total ambient noise could therefore serve as an alternative to the current approach. However, further research is required to demonstrate its accuracy and reliability. This filtering method is unlikely to be less accurate than current emission calculations.