Windar Photonics LiDAR Optimizes Wind Farms

Antoine Larvol, CTO of Windar Photonics, discusses how their continuous wave LiDAR technology enhances wind turbine performance through optimization and monitoring, increasing AEP and reducing loads, particularly for legacy turbines.

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Welcome to Uptime Spotlight, shining light on wind. Energy’s brightest innovators. This is the Progress Powering Tomorrow.

Alright, we’re here in Phoenix, a CP, clean power, uh, 2025. So I’m, uh. Sitting with Antoine Larvol from, he’s a CTO from Windar. Yep. Welcome to the show. Thank you. Uh, we’ve been, uh, happy enough to get actually to sit inside your booth where it’s nice and qui. Quiet and isn’t it nice? Yeah. We got glass behind the camera here and people are walking by, walking by, walking by.

Um, so this morning, uh, we, we talked yesterday a little bit about what wind photonics does. Yep. Of course, from our, uh, some of our other friends around the world. We’ve heard about some, some campaigns you’ve done in the United States, which have been. Really successful. So yeah, congrat good. Congratulations there.

Yeah, thank you. Um, and, and as, as a lot of things in the wind industry, Windar, photonics based in Denmark. 

Antoine Larvol: Yeah. 

Joel Saxum: So you guys, uh, bring it, bring in that Danish [00:01:00]technology. We’re here, of course, bringing it to the US market at a CP, the American Clean Power Show. So welcome to the States. Thank you. Um, it’s a short one, but a 

Antoine Larvol: good one.

Yeah, yeah, yeah, 

Joel Saxum: exactly. So, so I want to talk a little bit about what Windar photonics and, and it is a LIDAR based sensor, correct? 

Antoine Larvol: Yes. Right. So. We do continuous wave base, uh, lidar. Yep. Uh, main product is a two beam version mm-hmm. Where you shoot, uh, at 80 meters in front of the turbine. Mm-hmm. And you basically alternate from one beam to the other.

And measure wind speed and direction upfront, the, the turbine among others. 

Joel Saxum: Right. So we’re talking about, uh, if you, if you’re in the wind industry, you’ve ever seen these lidar units that are put actually, you’re the cell mounted, correct? Yes. Okay. Yeah. So, and, and, uh, we’re looking more on the optimization, retrofit monitoring side of things.

Yeah, 

Antoine Larvol: exactly. So we’ve never been a resource assessment company. Yeah. Or we don’t look at power curve verification and stuff like that. We really [00:02:00] focus on. Retrofitting those, existing turbines. And then add value to In terms of information to, the customer, Yeah. With the mon monitoring side of things.

Yeah. And, from day one, that’s been the goal of Windar Making something cheap, robust. That can just stay there and measure with good availability, wind speed, and direction coming to your turbine. 

Joel Saxum: I love it. so we wanna squeeze as much as we can outta these turbines. And you guys are increasing AEP that’s, the name of the game.

Yeah. Right. 

Increasing AEP below rated. and then above rated you decrease loads. Increase uptime. and we basically do that by going on the line of the wind direction. that you then feed to the turbine controller and then we can actually adjust the, yaw position of the turbine according to our information.

So I want to talk a little bit, we, we chatted a little bit offline about the, technology behind it, right? Yep. And people in the wind industry, if you’re around the wind industry around resourcing or you’re around optimization, you’ve heard [00:03:00] lidar. Yep. You know what I mean? And, but I don’t think.

A lot of people know exactly what lidar, what it does, how it does it. Yeah. What is the technical, where’s the magic coming from? Exactly. It’s just a black box. It’s just a, technically, I guess it’s just a white box. Yeah. For the wind photonics. But how do, how does the lidar work to measure actual wind speed coming into the turbine?

Antoine Larvol: Yeah, 

Joel Saxum: so 

Antoine Larvol: we basically focus laser light, and we do a focus point at 80 meters in front of the turbine. And basically there where your light concentrates on a specific location, then you hit particles in the air, pollen, water droplets, dust, whatever. Dust. Yeah. Okay. Whatever is there. And then you will, have a certain frequency of the light you emit, and that will just bounce on those particles and come back with a slight shift in frequency.

And that’s doppler shift. And then. Analyzing this shift, then you can derive a wind speed along the beam Of, the, [00:04:00] lidar. 

Joel Saxum: So 

Antoine Larvol: we’re 

Joel Saxum: talking about like a bunch of really, really smart trigonometry kind of 

Antoine Larvol: Yeah, exactly. I mean, you have a bit of optics. Yeah. Trigonometry. Uh, and, uh, yeah, it’s a bunch of optics.

Hardware, uh, software. A lot of software. A lot of software, yeah. Uh, to analyze that and squeeze as much info out of this. Right. We do, uh, you can derive wind speed, wind direction. You can look at turbulence. Mm-hmm. You can, uh, detect our wake, uh, is going on. So you can actually detect whether or not your turbine is in the wake.

Uh, and then based on that, then you will do different ing strategy in order to make the most of your turbine. Right. Decrease loads or increase, uh, outputs. 

Joel Saxum: Yeah. So and mean. That’s what, uh, the uptime podcast we’re here about. When we bring technology, we talk to. Smart people like yourself, Antoine. Thank you.

We, we want to pick the, pick out the solutions, right? Like what, how are you guys helping the wind industry? And that’s the important thing here. So we’ve [00:05:00] talked about two, basically, kind of two tracks that you guys go down and one of ’em is optimization. Yep. And one of ’em is monitoring. 

Antoine Larvol: Yep. 

Joel Saxum: So let’s, let’s start with optimization.

What does that look like from wind? 

Antoine Larvol: Yeah, I mean that’s a bit the unique part of wind. Uh, so we do lidar, uh, but actually the. Like this good selling product is this, uh, wind technology. So basically what it does is that we go on the line of the wind direction, uh, of the sensors from the primary secondary sensor from the turbine, and then we go on that line, read this info that they are measuring, and then, uh, correct this info according to our measurements.

Ah, okay. What’s going on out there is that, you know, those, uh, devices are placed behind the rotr. So they’re basically, uh, biased by the blades, basically turning in front and creating a lot of turbulence. You also, you have some effects depending on the, your misalignment of the turbine. And so basically you don’t have a great [00:06:00] measurement from, from those devices.

So what we bring is that, right. We measure upfront, so we are unbiased and then modify, the information, the wind direction information, and then feed that to the turbine controller so we can actually, yo. The turbine the way we want. 

Joel Saxum: So in a really simple way, you guys are creating what is an amazing wind speed and direction sensor.

Antoine Larvol: Yeah. So for this WindTIMIZER we only use wind direction information. And then basically improve what the turbine is given as information about. Relative wind direction of the nacelle Right. So yaw error. and then we correct that. So we increase, energy production below rated wind speed.

And then we have different strategies above rated wind speeds, aiming at reducing loads and increasing uptime. So the way we do that is actually we introduce small yaw misalignment, depending on wind [00:07:00] speed, in order to achieve that. Especially decreased loads on, blades.

Yeah. and the drive train rate. 

Joel Saxum: So in, let’s, talk a case study, right? We wanna, we always want to give examples, right? Yeah. So, so in the states we had, we talked about you guys are focusing on more, well not globally really, but you’re focusing on more of like the last generation of turbines, not the brand new ones.

Antoine Larvol: Yeah. So we are like really focusing at the moment on all this generation of. G 1, 15, 16. Two three. Yeah. 87 97 V 80, V 82. V 90, uh, seven, uh, maybe 2 92. Okay. Like this kind of turbines. Yeah. Uh, that’s been not there for, for a while. And basically where you don’t have a lot of offering on how to squeeze more power.

And they’re not supported anymore by manufacturers, right? Yeah. Uh, so that’s where we come in and offer that to. To be able [00:08:00] to, to produce more power, decrease load. Right. So the way we do that, usually when, uh, a new customer approach us is that you will do a deployment of like five units in a, in the farm.

Mm-hmm. Uh, basically pick randomly, uh, if, uh, five turbines and then we will install, uh, technology. You install like in three, four hours you’re done with installing the system. Oh, nice. On the tripod. Uh, wire that to the turbine controller. And then, uh, you will start this toggling campaign. So what we do is that we turn on the technology for 70 minutes and then turn it off 70 minutes.

This way you have a slight shift over of the period over the day. Mm-hmm. So you don’t always hit exactly the same time. So in case there’s, you know, any effect, recurring effect, and you go away from that, and then you will do this toggling on and off for like a period of like three, four months, depending on wind conditions.

And once you have like enough data on every wind, wind. You will analyze, uh, [00:09:00] what’s the power production difference when the union, the system is on versus off. Right. Okay. And then, um, yeah, compare that. Hopefully you gain some power. Yeah, yeah. Yeah. I mean, we know that those turbine out there, uh, actually, uh, arm is aligned, right?

So we know what to expect. I mean, we, uh, we did exactly that for some, uh, customers with, uh, V 80 twos. Mm-hmm. Uh, and you usually find like six and a half degree, uh, average. Oh, wow. Uh, your misalignment Wow. That you then correcting it, you, you reach like two, seven, 3%. Yeah. AP gain and uh, and that’s, so what we’ve done a while back with some customer, they verified the data.

We did our report at the time Vest was distributing for us. Mm-hmm. So they also did the report. We will agree. That was within 2.73% AP gain, man, I think. Yeah. And then they went to, to roll out the farm. Right. 

Joel Saxum: Wow. So they rolled out the whole farm after getting the two. Yeah. Well of course you would. Right.

That [00:10:00] that’s the business case. That makes sense. 

Antoine Larvol: I mean, you actually prove that you gained something. Yeah. So, hey, go 

Joel Saxum: for it. Right. So if I’m sitting on a V, you know, like if I’m just gonna run with that example of V 82, those are mostly probably installed. 2000 man, 2000, five to 2000 around that. Yeah. 10 ish.

So I’ve been, I’ve owned a wind farm. Say I’m in the States, I’ve owned a wind farm for. 10, 15, 20 years for whatever reason, I haven’t repowered it or whatnot. And all of a sudden someone comes along and says, I can get you two and a half, 3% more a EP. 

Antoine Larvol: Yeah. I mean, we are rolling out all those farms at the moment, right?

Yeah. I think we are on 60, 70% of the whole fleet in the US at the moment. Yeah. Yeah. 

Joel Saxum: I’m jumping on that if that’s me. Yeah. Um, you know, we had talked, like I said, off air. You guys have over a thousand units out. Yeah. But just in the last year, your, your deployments have ramped up big. 

Antoine Larvol: Yeah, we did like more than 500 I think last year.

Wow. I think we are doubling that this year. Yeah. 

Joel Saxum: Uh, 

Antoine Larvol: so 

Joel Saxum: good 

Antoine Larvol: for you guys. Yeah, it’s going great, doing 

Joel Saxum: big things for the industry. I like it. Um, so yeah, I guess we’ll for that moment if you have a V 82 wind farm or something Yeah. We 

Antoine Larvol: probably [00:11:00] contacted you already. Yeah. 

Joel Saxum: Yeah. Give these guys a call back.

Uh, ’cause they’re gonna get you more power and, uh, more revenue. I guess it’s at the end of the day. So optimization, you’re, you’re adjusting ya, you’re, you’re correcting for loads. You’re, you’re fixing some of these things, but there’s also a monitoring piece to this. 

Antoine Larvol: Yeah. So that actually came, uh, from those rollouts we did.

Um, there’s like some security concerns when you go and actually install those hundreds of units on the farm. That’s, that’s huge in the United States. Anything you’re putting on right now? Exactly. Cybersecurity. Cybersecurity. So toing campaign, we, we usually do modems. Uh, 3G uh, modem connected to the. To the unit, but that doesn’t fly when you do rollouts.

Right? So what we did, uh, is actually then package all our monitoring tool, reporting tool data, graving tool and processing, uh, all kind of, uh, alarm flags in case like something goes wrong with any of those units. So we packaged all this into a an [00:12:00] os. Mm-hmm. Uh, and then we can directly deploy that on customer servers.

And then this way they can monitor the lighter fleet. Mm-hmm. Uh, real time and then generate reports, uh, and see that basically those units are, are running fine. Right? Yeah, yeah. Everything on, on premise so that it’s not Yeah, exactly. You’re not worrying. Exactly. So we don’t have access to it. Uh, it’s fuel integrated, integrated in the network, and we just hands off Yeah.

Let the unit run, you know? Yeah. Uh, and then as part of that. Then we also developed some new modules that we produce, uh, we propose to the customer and, uh, yeah, one of them, for example, is this, uh, turbine performance monitoring module where, you know, we have very good wind speed data. Uh, we can actually measure also, uh, rotation, rotational, uh, rotational speed of the turbine.

And then, uh, mixing those two, having air density. Then you can actually track performance of, of the turbine over time. Okay. Uh, within [00:13:00] plus, minus 0.5%. Wow. Um, and then, yeah, that’s basically that. Right. You will see then, uh, whether, you know, you have some kind of leading edge degradation Yeah. On certain specific units.

And then we are looking into, you know, looking at turbines individually, but eventually. In some mirror, whatever. Uh, look at turbines like once against the other as well. Yeah. Um, we integrating, uh, some rotor balance detection, for example. Okay. Uh, so we just trying to add more and more value to the customer so that they have a proper view of the asset.

Right. Yeah. Usually what we see that people do in the industry is that, you know, they look at scada, right? Yeah. That’s what you have, that’s what you can do. Uh, so. You do power curve assessment and whatnot with your scada, so you believe in this wind speed, wind direction from those an anemometers. Mm-hmm.

But we know by experience that that’s not accurate. Right? Right. So you might end up making, [00:14:00] um, wrong decision because you’re just given wrong information. Right. And you have companies out there, you know, they’re doing ai, machine learning, modeling, whatever. Great. But. You know, if the data is wrong, uh, yeah, you can do whatever you want.

You will reach wrong conclusions, right? Yeah. Uh, so that’s what we bring value. We actually have the. The proper wind speed so we can do properly the monitoring. Right. 

Joel Saxum: Uh, what are the, the saying, the old saying comes to mind for me? Like, you can’t make chicken soup out of chicken poop. Right. So if you, something like that.

So if you don’t have good data coming in, sometimes all the, all the AI models in the world and things, you can make 

Antoine Larvol: great modeling, whatever. Yeah. But it may not be true. Right? Yeah. 

Joel Saxum: And I don’t wanna to, I don’t wanna like stop that idea ’cause I think it’s great as we move forward. No, no. I mean, we have nothing 

Antoine Larvol: against the idea.

Joel Saxum: It is just. 

Antoine Larvol: You know, you’re using wrong info, right? Yeah. So you’re gonna 

Joel Saxum: lead to wrong conclusions and that’s it. Right? So in my mind, I’m thinking about another, another use case for the window, like the windows, the monitoring portion of it. And it is, you talk to all of these [00:15:00] people about when you, when you mentioned erosion monitoring for per performance.

When you talk to all these people about, uh, what’s the performance degradation of, of this erosion happening on the leading edge or. If we put LEP on, do we get this performance back and stuff? And I’m thinking, I’m, I’m talking to all the researchers that are listening to this. You guys should be using your sensors to validate a lot of this research that’s going on, on these wind turbines.

Yeah. I mean, you, you track, 

Antoine Larvol: I mean, since you have the LIDAR out there anyway, you know, yeah. You might as well use the data and then track properly how your asset is doing over time. Right. Um. And that’s the whole idea, right? Squeezing most information from Yep. What we have, right? Yep. And you know, we know turbulence, uh, conditions.

We know wind speed. We know, uh, wake, whether it’s wake or not. Mm-hmm. We know air density. We just mm-hmm. Put some module there and then you can just basically track then your performance over time. Right? Yeah. Uh, and then have this done [00:16:00] automatically. Mm-hmm. Uh, and then I just have, go check a dashboard.

And be like, okay, past six months, how has this turbine been doing? Mm-hmm. Okay, great. You just stay nice and clean and then suddenly you can pinpoint and target exactly the one that goes wrong in the farm. Right? Yeah. And then, I mean, we are not going to fix your leading edge erosion. Right. You still need to have, go out there, repair it yourself, right?

Yeah. But at least we can point out Okay. There’s a problem there. Right? Yeah. 

Joel Saxum: You know, and one of the things I wanna make sure we touch on is we brushed past the cybersecurity thing. Yeah. Talking with all of the asset owners that I deal with every day, friends in the industry, CMS companies, you name it.

Cybersecurity is becoming more and more of a frontline issue within the wind industry. Yep. Everybody’s concerned about it. Anything you try to put on a turbine, especially if it’s connected to a controller, connected to electronics, it’s over the air, it’s on the cloud. Everybody’s ah, everybody has their hair up on the back of their neck about it.

So you guys have taken the risk [00:17:00] out of that thing. By putting everything on premise. Yeah. I 

Antoine Larvol: mean we, we’ve, it’s our own tools. yeah. At window we always develop our own tools in the house. we have a great software team doing that, and, and then they basically package the just stuff we’ve been developing for the past 10 years.

Yeah. Into an OS and that you can just deploy at the customer. And then this way you have like automatic reporting about your lidars. your turbines. You can, directly interact with those lidars, turn them on, turn them off, start those towing campaign, get some, information about how much AP you gained, stuff like that.

Joel Saxum: Fantastic. 

Antoine Larvol: Yeah. 

Joel Saxum: so message that I want to get out to the wind industry, if you, have some turbines that are legacy turbines that are out there. And you wanna get two point a half to 3% more revenue generated outta those turbines. Yep. You need to call wind photographers and a bit more time, I mean, less loads.

Yeah, less [00:18:00] loads. Extend the life of those blades. I mean, we know that those 

Antoine Larvol: turbines are, have some issues. Right. So it’s, uh, it’s pretty straightforward to make a business case. Right. Fantastic. So Antoine, how do people get ahold of you? Uh, we have a website. We are online. We are on LinkedIn. Okay. Uh, so yeah, you just go on the window, photonics.com and, uh.

And then reach out to us. Right. Fantastic. 

Joel Saxum: Well, Antoine, thanks for, uh, joining the Uptime podcast here. Thanks a lot again, live here from, uh, in Phoenix. It’s hot outside, so let’s stay inside. Yes, and I’d read it. Yeah. Thank you.