Coordinated Optimal Power Planning of Wind Turbines in a Wind Farm

Wind energy is on an upswing due to climate concerns and increasing energy demands on conventional sources. Wind energy is attractive and has the potential to dramatically reduce the dependency on non-renewable energy resources. With the increase in wind farms there is a need to improve the efficiency in power allocation and power generation among wind turbines. Wake interferences among wind turbines can lower the overall efficiency considerably, while offshore conditions pose increased loading on wind turbines. In wind farms, wind turbines* wake affects each other depending on their positions and operation modes. Therefore it becomes essential to optimize the wind farm power production as a whole than to just focus on individual wind turbines. The work presented here develops a hierarchical power optimization algorithm for wind farms. The algorithm includes a cooperative level (or higher level) and an individual level (or lower level) for power coordination and planning in a wind farm. The higher level scheme formulates and solves a quadratic constrained programming problem to allocate power to wind turbines in the farm while considering the aerodynamic effect of the wake interaction among the turbines and the power generation capabilities of the wind turbines. In the lower level, optimization algorithm is based on a leader-follower structure driven by the local pursuit strategy. The local pursuit strategy connects the cooperative level power allocation and the individual level power generation in a leader-follower arrangement. The leader, could be a virtual entity and dictates the overall objective, while the followers are real wind turbines considering realistic constraints, such as tower deflection limits. A nonlinear wind turbine dynamics model is adopted for the low level study with loading and other constraints considered in the optimization. The stability of the algorithm in the low level is analyzed for the wind turbine angular velocity. Simulations are used to show the advantages of the method such as the ability to handle non-square input matrix, non-homogenous dynamics, and scalability in computational cost with rise in the number of wind turbines in the wind farm

Pengembangan Strategi Kontrol Optimal Pada PMSG Wind Turbine Melalui Sistem Penyimpan Energi Berbasis Algoritma Swarm Intelligence

Ketersediaan energi fosil yang semakin menipis dan pencemaran emisi gas rumah kaca sebagai akibat penggunaan bahan bakar fosil menuntut untuk dikembangkan energi terbarukan. Salah satu energi terbarukan yang memiliki potensi di Indonesia yaitu energi angin. Sumberdaya energi angin di berbagai wilayah Indonesia berkisar antara 2,5 – 5,5m/s dan termasuk dalam kategori kecepatan angin kelas rendah hingga menengah. Kelemahan pemanfaatan energi angin yaitu kecepatan angin yang berfluktuasi dengan cepat dan tidak menentu sehingga akan menghasilkan daya yang berfluktuasi pula tergantung pada kecepatan angin. Sistem konversi energi angin (SKEA) akan menghasilkan daya optimal jika beroperasi pada titik daya maximum sehingga kontrol optimal dan maximum power point tracking dibutuhkan untuk mendapatkan daya optimal. Tujuan dari penelitian adalah mendesain kontrol optimal pada sistem konversi energi angin menggunakan PMSG untuk mengatur aliran daya yang dihasilkan turbin angin ke grid dengan menggunakan baterai sebagai penyimpan energi. Beberapa metode MPPT yang terdiri dari metode konvensional, artificial intelligence dan swarm intelligence dikembangkan untuk dapat diimplementasikan pada SKEA dan performansi antara metode tersebut dibandingkan. Untuk mengurangi fluktuasi daya yang disebabkan oleh energi angin yang tidak menentu dan tidak dapat diprediksi, sebuah baterai dihubungkan ke dc-link kapasitor melalui konverter bidirectional yang dikontrol oleh kontroler PI. Sistem penyimpan energi baterai akan mempertahankan keseimbangan daya selama kelebihan atau kekurangan antara daya yang dihasilkan dan perubahan beban. Metode MPPT dengan algoritma modified P&O diimplementasikan dalam perangkat keras menggunakan mikrokontroler pada prototipe SKEA. Berdasarkan hasil simulasi, algoritma modified firefly memiliki performansi teraik untuk mengektraks daya optimal dibandingkan dengan metode P&O dan PSO. Sedangkan implementasi algoritma MPPT pada prototipe SKEA stand alone menunjukkan algoritma modified P&O menghasilkan efisiensi rata-rata sebesar 91.7% sedangkan algoritma modified Firefly menghasilkan efisiensi rata-rata sebesar 89.15%. Efisiensi terendah dihasilkan oleh algoritma P&O sebesar 80.05% dengan fluktuasi yang lebih besar =================================================================== Availability of diminishing fossil energy and increasing greenhouse gas pollution as result of use of fossil fuel, demand the development of renewable energy. Wind Energy is one of the renewable energy that has the potential in Indonesia. Wind energy resources in various areas Indonesia ranged between 2.5 - 5,5m/s, including wind speed categories of low to medium grade. Weakness of wind energy utilization is wind speeds that fluctuate rapidly and intermittent so that will produce power which fluctuate and depend on wind speed. The wind energy conversion system (WECS) will produce optimal power when operating at maximum power point so optimal control and maximum power point tracking is required to obtain optimal power. The research purpose is to design optimal control on wind energy conversion system using PMSG to regulate the power generated by wind turbine to grid using battery as energy storage. Some MPPT methods consisting of conventional methods, artificial intelligence and swarm intelligence are developed to be implemented on SKEA and the performance between the methods is compared. To reduce the power fluctuations caused by the intermittent and largely unpredictable nature of wind energy, a battery is connected across the dc-link capacitor via a bidirectional converter that is controlled by a PI controller. The battery storage system assists in maintaining the power balance during surplus or deficit generation conditions and load changes. The MPPT method with P & O modified algorithm is implemented in hardware using microcontroller on SKEA prototype. Based on the simulation results, the modified firefly algorithm has the best performance to extract the optimal power compared to the P&O and PSO methods. While the implementation of the MPPT algorithm on the stand alone WECS prototype shows the modified P&O algorithm yields an average efficiency of 91.7% while the modified Firefly algorithm yields an average efficiency of 89.15%. The lowest efficiency is generated by the P & O algorithm of 80.05% with greater fluctuation