This work pertains to integration of supercapacitor energy storage system with wind-penetrated two-area power system. A small-sized SCES device is incorporated in automatic generation control of each area. For robust and optimum usage of SCES, a customized neuro-adaptive predictive control model is developed using level 1 S-function coding. This model feeds reference power command to the converter of storage system with its voltage used as feedback loop. A step load disturbance reflecting load outage is introduced back to back in the power system. It is seen through simulation studies carried out in MATLAB Simulink Environment that there is significant improvement in frequency response and tie power deviations. SCES converter power limits are fully maintained; besides, SCES voltage and power response is also depicted.

这项工作涉及超级电容器储能系统与透风两区电力系统的集成。每个区域的自动发电控制中都集成了小型 SCES 设备。为了稳健和最佳地使用 SCES，使用 1 级 S 函数编码开发了定制的神经自适应预测控制模型。该模型以其电压作为反馈回路向存储系统的转换器馈送参考功率命令。反映负载中断的阶跃负载扰动被引入电力系统中。通过在 MATLAB Simulink Environment 中进行的仿真研究可以看出，频率响应和领带功率偏差有显着改善。完全维持 SCES 转换器功率限制；此外，还描绘了 SCES 电压和功率响应。