Currently, there are more than 760 wind turbines in operation in Australia. Together, these generate nearly 1.5 gigawatts (GW) of power, or roughly 1.3 per cent of the electricity now being consumed in Australia.
Achieving reliable and profitable wind farm energy production is fraught with technical and economic risk.
Risk assessment must take into account variability in the wind profile measured at the site, the location of wind turbines, the technology of the turbines, the availability of the turbines, and the operational characteristics of the wind farm management and control system.
Once the wind farm is operational, data gathered from the monitoring system can be analysed to identify and quantify any discrepancies between predicted and actual power production.
The process of addressing these discrepancies based on measured data from a wind farm already in operation, is a time-consuming and expensive effort.
Detailed simulation models, however, can streamline this process by providing engineers with more insight into system behaviour in a cost-effective and repeatable manner. This article explores some of the benefits that simulation can bring during the life cycle of a wind farm, from specification and design to in-service support.
Benefits of simulation
Modelling and simulation studies of a wind farm offer a means to mitigate technical and operational risk by enabling the engineering team to explore design trade-offs, assess control and management system operation, estimate achievable production with confidence , and perform fault studies in a safe and repeatable environment.
Simulation lets wind farm developers see, at an early stage, system responses that might not otherwise be seen until the integration and commission stage.
If issues that are detrimental to achieving the production goal of the wind farm are found in the integration and commission stage, they take much longer and cost more to correct. Through simulation, engineers can identify issues earlier in the process, before the commitment of significant capital expenditures, so that remedial action may be taken in a time-efficient and cost-effective manner.
Further benefits accrue to engineering teams that reuse the simulation models to support in-service operation. Models can be used to diagnose unexpected system behaviour, assess upgrades to control systems or the system architecture, and evaluate wind farm expansion plans.
Traditionally organisations often carry out the modelling and simulation studies of a wind farm in disparate software packages where each software package addresses one particular domain and/or aspect of wind farm design and performance.
Software tools that enable engineers to share a common environment offer a considerable advantage in improving project efficiency. In such an environment, the simulation model serves as an integrated reference upon which different engineering groups can conduct their design tasks and communicate with each other to evaluate overall system performance early in the development process.
Further, as the wind farm moves through its life cycle, the simulation model evolves through continual refinement and verification, and ultimately becomes an indispensable in-service support tool.
Simulation using data-driven and physics-based models
One advantage afforded to wind farm developers is that measured data is available well ahead of installation. Data from the wind site assessment can be used to drive the inputs to a wind farm simulation model.
Aggregate wind farm models, which represent a wind farm as a single turbine equivalent, are useful in assessing behaviour at the grid point-of-connection (POC).
However, these aggregate models fail to capture the variability of power production across the site, including the availability of reactive power from each turbine. Reactive power availability is crucial in supporting system voltage and achieving low-voltage ride-through requirements.
To enhance model fidelity in the simulation, specific wind turbine technology and wind profiles from different locations at the site can be included.
Measured wind velocity and directional data can also be used to account for anticipated turbulence to the wind profile and determine turbine sitting and spacing to optimise turbine efficiency and overall power production for a given site.
Bringing measured data into the simulation environment serves another benefit; specifically, it reduces the time needed to develop a simulation model that is useful as an engineering tool. That is, by driving physical models with measured data, engineers can rapidly realise a verified model.
Specific applications of simulation in wind farm design and pperation
Simulating Wind Farm Response in Low-Voltage Conditions
Recent requirements mandate that a wind farm must remain connected in post-fault and low voltage conditions. The control sequence that a wind farm can execute during such conditions may vary. For example, a wind farm may:
1. Disconnect during a fault and reconnect post-fault.
2. Stay connected during the fault and post-fault.
3. Either (1) or (2) followed by providing reactive power post-fault to support voltage recovery.
4. Some other control sequence that benefits wind farm operation and maintains compliance with grid code.
These conditions cannot be sufficiently assessed with an aggregate model because the low voltage response at the POC is dependent on the available reactive power capacity on each turbine and any additional voltage support device.
As a result, engineers must use a wind farm model with individual wind turbines and a detailed representation of the management and control systems to assess the chosen control sequence.
The reactive power capacity is unlikely to be consistent across the individual turbines due to wind variability and the resultant variability in the active power produced by each turbine across the site. In addition to assessing electrical performance, the simulation model can also be used to verify that the turbine control software and wind farm management system software meets the stringent requirements for low-voltage conditions.
Ensuring that proper component ratings are used in design reduces costs in two ways. It can reduce costly downtime and service calls during a turbine’s operational life due to components that do not have sufficient rating to handle operational requirements.
It also reduces capital expenditures because the use of components with excess capacity is minimised. As an example, consider an example wind farm with 40 individual Type III turbines.
Figure 1 shows the result of a simulation of this wind farm. Reactive power in this example is provided only by the rotor side power converters. The response at the POC following a brownout condition on the grid of 90% nominal voltage for two different scenarios is shown.
In the first scenario, the wind speed across the site is relatively high, and thus there is little reactive power capacity in the turbine converters. In the second scenario, the wind speed is lower, thus allowing higher levels of reactive poer to be generated. The simulation reveals that the voltage collapses for the high wind speed condition.
Note that reactive power capacity for the higher wind speed is depleted after eight seconds, causing voltage to collapse. The simulation results in this example strongly suggest that the components do not have sufficient rating to meet operational requirements.
Based on these results, the design team might consider either higher rated converters for the turbines or a supplementary voltage support device.
Simulations of this type are important in determining the required capacity of system components to meet specific production goals and comply with grid code.
Figure 1: Comparison of wind farm output for different wind conditions.
Improving wind farm response to subsynchronous resonance
Subsynchronous resonance (SSR) is a concern for wind farm operators and grid operators alike. While this phenomenon is most often associated with large thermal plants, large-scale wind farms that are connected to series compensated networks can also exhibit SSR.
The occurrence of SSR increases mechanical fatigue of the turbine shafts, can lead to shaft failure, and reduces the availability of the turbines.
The traditional approach to mitigating SSR is to integrate a series compensation device onto the electric grid. However, with power converter technology connected to each turbine, there is an opportunity to apply innovative control techniques and mitigate the effect of SSR locally at each turbine.
For example, SSR can be mitigated by adding an auxiliary signal to the electromagnetic torque control signal to the rotor-side converter of a doubly-fed induction generator (DFIG) turbine.
Figure 2 alongside shows simulation results of this auxiliary damping applied to a single wind turbine test case.
This example highlights the value of developing, simulating and applying innovative control strategies to mitigate undesirable power system phenomena.
There are many risk factors that can prevent a wind farm from achieving a given energy production goal.
Detailed simulation offers a means to manage these risks by enabling the engineering team to optimise wind turbine placement, design and verify control software, select appropriately rated components, avoid unnecessary capital expenditures, reduce unplanned downtime and maintenance costs, respond to detrimental power system phenomena, and support all phases of the wind farm life cycle.
[Dr. Graham Dudgeon is the energy production industry marketing manager at MathWorks. He received his M.Eng. in avionics and Ph.D. in multivariable control from the University of Glasgow.]