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Modelling for
Performance and Controls

Control system design revolves around creating a clear transient model for the system being
controlled. This model should accurately represent all relevant dynamics while avoiding unnecessary complexity. Our expertise lies in defining, implementing, and parameterizing dynamic models across diverse domains, encompassing:

These models facilitate early-stage system simulations, enabling cost-effective design iterations to assess and enhance system performance and operability. By minimizing complexity and computational load, these models are well-suited for extensive simulations. This capability allows the representation of a system’s behavior over its entire lifespan, thereby quantifying the complete spectrum of expected performance.

Case studies

Goal statement:

In a thermal energy storage plant, control algorithms play a primary role in achieving:

  • Best Round Trip Efficiency (RTE)
  • Compliance with grid norms
  • Plant operational capabilities


At the same time, such algorithms also keep plant operating parameters within the design limitations of plant components, such as turbomachinery, heat exchangers, tanks, piping, etc.

Solution provided:

A comprehensive dynamic model of the plant is built, integrating equations from different domains (thermodynamic, electrical, etc.). The optimum trade-off in model complexity is set taking into account PFDs and P&IDs. Such model is built to be interfaced in closed loop with the control software. Hence a framework for developing, maintaining and versioning the model, the control software and its tunings is put in place. Control software architecture and functionalities are then developed with fast iterations allowed by the model-in-the-loop simulations. The simulation iterations are made more powerful by a pre/post processing toolchain. The preprocessing allows agile parallel execution of a high number of simulations, which combined represent the whole spectrum of the plant operations and environmental variabilities, without relying on human input unless strictly needed. The post-processing toolchain operates on the executed simulations to produce automatically generated reports, optimizing the use of analysts’ time.

Control functionalities integrate the whole spectrum of traditional closed loop solutions, model based optimum controls, estimation of non measurable variables, along with the discrete domain solutions such as finite state machines and algebraic logics. Algorithms are tuned to comply with requirements from all the above-mentioned sources (RTE, grid norms, operability). Compliance to requirements is documented for all internal customers and external partners.

The control code is documented for EPC specifications where a DCS target is contemplated, while other code fractions are used to directly generate and deploy a PLC code. Field commissioning and performance
validation follow.

Problem statement:

Wind energy is renewable and environmentally friendly. Its main weakness is that when the wind speed drops, so does the power that a wind turbine can deliver to the grid. A wind turbine manufacturer wants therefore to explore possibilities to make the power delivery of their plants immune to the natural oscillations of the wind by means of energy storage electrical devices to buffer power between the turbine and the grid. The chosen energy storage devices are super- capacitors, and Li-Ion batteries to fit into the turbine tower.

Solution provided:

We design, implement and run the dynamic models of such energy storage devices. We integrate them with the turbine model to assess the power delivery at low voltage stage. The study seeks and achieves optimum size and combination of super-capacitors and batteries to leverage their respective strengths.
Upon this result, the business case is made.

Problem statement:

When estimating parameters of a mechanical system in resonance, it is typically the damping value that poses the biggest challenge, more so than the inertial and stiffness values. A wind turbine manufacturer questions its own design conditions when actual first-mode tower oscillations are suspected to be less structurally damped than was hypothesized during the development phase. The most significant source of tower oscillations damping in wind turbines are the aerodynamic properties of the rotor themselves, meaning that the tower structural damping is even less immediately observable in the tower top acceleration data.

Solution provided:

The tower top acceleration data is preselected according to the following validity conditions:
Blades are at feather position, right after a stop manoeuvre. This allows having the minimum aerodynamic damping factor in the fore-aft motion, while the elastic energy released in the stop manoeuvre is still relevant and exciting the fore-aft motion itself.

There is observable fore-aft tower top motion, while there is no relevant energy transfer to the side- side motion where aerodynamic damping would still intervene. Side-side motion has a neglectable amplitude. The fore-aft tower top acceleration can then be used, under the conditions above, to estimate the inertial, elastic and damping factors associated to the first mode of vibrations of the tower, according to a classic least-square-error methodology exploring the space of parameters. The identified damping value is 25% lower than the design hypothesis. Design simulations are re-run accordingly, resulting in a non-neglectable increase in tower bottom fatigue. The early integration of knowledge from field data back into the design allows for a timely deployment of further active damping control strategies, bringing plant operations back within the fatigue design envelope, thereby ensuring the certified longevity of the turbine’s structure.