Deep neural learning based distributed predictive control for offshore wind farm using high fidelity LES data

The paper explores the deep neural learning (DNL) based predictive control approach for offshore wind farm using high fidelity large eddy simulations (LES) data. The DNL architecture is defined by combining the Long Short-Term Memory (LSTM) units with Convolutional Neural Networks (CNN) for feature extraction and prediction of the offshore wind farm. This hybrid CNN-LSTM model is developed based on the dynamic models of the wind farm and wind turbines as well as higher-fidelity LES data. Then, distributed and decentralized model predictive control (MPC) methods are developed based on the hybrid model for maximizing the wind farm power generation and minimizing the usage of the control commands. Extensive simulations based on a two-turbine and a nine-turbine wind farm cases demonstrate the high prediction accuracy (97% or more) of the trained CNN-LSTM models. They also show that the distributed MPC can achieve up to 38% increase in power generation at farm scale than the decentralized MPC. The computational time of the distributed MPC is around 0.7s at each time step, which is sufficiently fast as a real-time control solution to wind farm operations

Frequency Performance Assessment of Future Grids

Future grids security will be challenged by the increasing penetration of non-synchronous renewable energy sources (NS-RES). Studies of future grids with high penetration of NS-RES suggest that along with other issues, system frequency control will become a challenging task. Therefore, this thesis first, studies the impact of high penetration of NS-RES and different penetration levels of prosumers on the performance and frequency stability of the Australian national electricity market (NEM). By doing this, the connection between the NS-RES and the system frequency performance, as well as different penetration levels of prosumers and the system frequency performance are quantified. Second, we propose a frequency performance assessment framework based on a timeseries approach that facilitates the analysis of a large number of scenarios. This framework is utilised to assess the frequency performance of the Australian future grid by considering a large number of future scenarios and sensitivity of different parameters. By doing this, we identify a maximum non-synchronous instantaneous penetration range for the system from the frequency performance point of view. Then, to improve the frequency performance of the system with high penetration levels of NS-RES, we evaluate the contribution of different resources, such as synchronous condensers, wind farm’s synthetic inertia and a governor-like response from the de-loaded wind farms, on frequency control. The results show that the last one adds more flexibility to the system for frequency control. Finally, a coordinated operation strategy for wind farms is proposed. It is shown that by operating the wind farm in a coordinated way, we can increase both the output power and the rotational kinetic energy of the wind farm. Time-domain simulations show that the proposed operation strategies noticeably improve the wind farm’s performance in frequency control

Wind Farm Coordinated Control and Optimisation

This thesis develops and implements computationally efficient and accurate wind farm coordinated control strategies increasing energy per area by mitigating wake losses. Simulations with data from the Brazos, Le Sole de Moulin Vieux (SMV) and Lillgrund wind farms show an increase of up to 8% in farm production and up to 6% in efficiency. A live field implementation of coordinated control strategies show that curtailing upstream turbine by up to 17% in full or near-full wake conditions can increase downstream turbine’s production by up to 11%. To the best knowledge of the author, this is the first practical implementation of Light Detection And Ranging (LiDAR) based coordinated control strategies in an operating wind farm. With coordinated control, upstream turbines are curtailed using coefficient of power or yaw offsets in such a way that the decrease in upstream turbines’ production is less than the increase in downstream turbines’ production resulting in net gain. This optimum curtailment is achieved with on-line coordinated control which requires an accurate and fast processing wind deficit model and an optimiser which achieves the desired results with high processing speed using minimum overheads. Performance evaluation of carefully selected optimisers was undertaken using an objective function developed for increasing farm production based on coordinated control. This evaluation concluded that Particle Swarm Optimisation (PSO) is the most suitable optimiser for on-line coordinated control due to its high processing speed, computational efficiency and solution quality. The standard Jensen model was used as a starting point for developing a fast processing and accurate wind deficit model referred to as the Turbulence Intensity based Jensen Model (TI-JM), taking wake added turbulence intensity and deep array effect into consideration. The TI-JM uses free-stream and wake-added turbulence intensities for predicting effective values of wake decay coefficients deep inside the farm. This model is validated using WindPRO and data from three wind farms case studies as benchmarks. A methodology for assessing the impact of wakes on farm production is developed. This methodology visualises wake effects (in 360°) by calculating power production using data from the wind farms (case-studies). The wake affected wind conditions are further analysed by calculating relative efficiency. The innovative coordinated control strategies are evaluated using data from the wind farms case studies and WindPRO as benchmarks. A live field implementation of coordinated control strategies demonstrated that the production of downstream turbines can be increased by curtailing upstream turbines. This field setup consisted of two operating wind turbines equipped with modern LiDAR. Analyses of the high frequency real time data were performed comparing field results with simulations. It was found that simulations are in good agreement (within a range of 1.5%) with field results

Determining the Wind Speed Distribution within a Wind Farm considering Site Wind Characteristics and Wake Effects

This paper introduces a wind speed model for simulating the distribution of wind speeds within a wind farm. The model combines a macro scale wind speed time series (WSTS) model based on a continuous Markov process with a wake flow model, based on the Jensen model, to produce wind speeds upwind of every wind turbine. This model has been designed for use in the testing of turbine coordinated control algorithms and for use in detailed reliability analysis. An example analysis was carried out to investigate the Annual Energy Not Produced (AENP) due to wake effects on a single string wind farm. It was found that the wakes accounted for a 20.2% reduction in energy production compared to the wakeless scenario, highlighting the need to model these wake effects

Wind Power Integration into Power Systems: Stability and Control Aspects

Power network operators are rapidly incorporating wind power generation into their power grids to meet the widely accepted carbon neutrality targets and facilitate the transition from conventional fossil-fuel energy sources to clean and low-carbon renewable energy sources. Complex stability issues, such as frequency, voltage, and oscillatory instability, are frequently reported in the power grids of many countries and regions (e.g., Germany, Denmark, Ireland, and South Australia) due to the substantially increased wind power generation. Control techniques, such as virtual/emulated inertia and damping controls, could be developed to address these stability issues, and additional devices, such as energy storage systems, can also be deployed to mitigate the adverse impact of high wind power generation on various system stability problems. Moreover, other wind power integration aspects, such as capacity planning and the short- and long-term forecasting of wind power generation, also require careful attention to ensure grid security and reliability. This book includes fourteen novel research articles published in this Energies Special Issue on Wind Power Integration into Power Systems: Stability and Control Aspects, with topics ranging from stability and control to system capacity planning and forecasting

Wind turbine wakes for wind energy

During recent years, wind energy has moved from an emerging technology to a nearly competitive technology. This fact, coupled with an increasing global focus on environmental concern and a political desire of a certain level of diversification in the energy supply, ensures wind energy an important role in the future electricity market. For this challenge to be met in a cost-efficient way, a substantial part of new wind turbine installations is foreseen to be erected in big onshore or offshore wind farms. This fact makes the production, loading and reliability of turbines operating under such conditions of particular interest