ﮐﻨﺘﺮل ﻓﺮﮐﺎﻧﺲ-ﺑﺎر رﯾﺰﺷﺒﮑﻪ دو ﻧﺎﺣﯿﻪ اي ﺑﻪ وﺳﯿﻠﻪ ﮐﻨﺘﺮل ﮐﻨﻨﺪه ﭘﯿﺶ ﺑﯿﻦ ﻣﺪل ﺑﻬﯿﻨﻪ ﺷﺪه

Load frequency control in a two area microgrid by Optimized model predictive controller

ﭼﮑﯿﺪه: ﮐﻨﺘﺮل ﺑﺎر-ﻓﺮﮐﺎﻧﺲ در رﯾﺰﺷﺒﮑﻪ اﯾﺰوﻟﻪ ﺑﺴﯿﺎر ﻣﻬﻢ اﺳﺖ، ﺑﺎ اﻏﺘﺸﺎش ﺑﺎر، ﻓﺮﮐﺎﻧﺲ رﯾﺰﺷﺒﮑﻪ ﻧﻮﺳﺎن ﭘﯿﺪا ﻣﯽ ﮐﻨﺪ و اﯾﻦ ﻧﻮﺳﺎن ﺑﺎﯾﺪ ﺗﻮﺳﻂ ﺳﯿﺴﺘﻢ ﮐﻨﺘﺮل ﺑﺎر-ﻓﺮﮐﺎﻧﺲ)ﮐﻨﺘﺮل ﺛﺎﻧﻮﯾﻪ( ﻣﯿﺮا ﮔﺮدد. در اﯾﻦ ﻣﻘﺎﻟﻪ ﺗﻌﺪاد ﮐﻨﺘﺮل ﮐﻨﻨﺪه ﻫﺎ ﻣﻮرد اﺳﺘﻔﺎده ﺑﺮاي ﻣﻨﺎﺑﻊ ذﺧﯿﺮه اﻧﺮژي ﻣﻮﺟﻮد در رﯾﺰﺷﺒﮑﻪ ﮐﺎﻫﺶ ﭘﯿﺪا ﮐﺮده اﺳﺖ)ﭘﯿﭽﯿﺪﮔﯽ ﮐﻤﺘﺮ( و از ﮐﻨﺘﺮل ﭘﯿﺶ ﺑﯿﻦ ﻣﺪل )MPC( ﮐﻪ ﭘﺎراﻣﺘﺮﻫﺎي وزن آن ﺑﺎ اﺳﺘﻔﺎده از اﻟﮕﻮرﯾﺘﻢ ﺗﺮﮐﯿﺒﯽ ﺟﺪﯾﺪ ازدﺣﺎم ذرات ﻣﺒﺘﻨﯽ ﺑﺮ دﯾﻮاﻧﮕﯽ-ﺟﺴﺘﺠﻮي اﻟﮕﻮ ﺑﻬﯿﻨﻪ ﺷﺪه )HCRPSO-PS(، ﺑﺮاي ﮐﻨﺘﺮل ﺑﺎر-ﻓﺮﮐﺎﻧﺲ در رﯾﺰﺷﺒﮑﻪ دو ﻧﺎﺣﯿﻪ اي اﺳﺘﻔﺎده ﺷﺪه اﺳﺖ. ﻧﺘﺎﯾﺞ ﮐﻨﺘﺮل ﮐﻨﻨﺪه ﭘﯿﺸﻨﻬﺎدي، در ﭼﻨﺪ ﺳﻨﺎرﯾﻮ ﻣﺨﺘﻠﻒ و ﺑﺎ در ﻧﻈﺮ ﮔﺮﻓﺘﻦ ﺗﻐﯿﯿﺮات ﺑﺎر و ﻣﻨﺎﺑﻊ ﺗﻮﻟﯿﺪ ﭘﺮاﮐﻨﺪه رﯾﺰﺷﺒﮑﻪ ﺑﺎ روش ﻫﺎي ﻣﺎﻧﻨﺪ ﮐﻨﺘﺮل ﮐﻨﻨﺪه PID ﺑﻬﯿﻨﻪ ﺷﺪه ﺑﺎ اﻟﮕﻮرﯾﺘﻢ رﻓﺘﺎر اﺟﺘﻤﺎﻋﯽ ﻋﻨﮑﺒﻮت )SSO( و ﮐﻨﺘﺮل ﮐﻨﻨﺪه ﭘﯿﺶ ﺑﯿﻦ ﻣﺪل ﮐﻪ ﭘﺎراﻣﺘﺮﻫﺎي وزن آن ﺑﺎ اﻟﮕﻮرﯾﺘﻢ ازدﺣﺎم ذرات ﻣﺒﺘﻨﯽ ﺑﺮدﯾﻮاﻧﮕﯽ )CRPSO( )ﺑﺮاي ﻧﺸﺎن دادن ﮐﺎراﯾﯽ اﻟﮕﻮرﯾﺘﻢ ﺗﺮﮐﯿﺒﯽ ﺟﺪﯾﺪ(، ﻣﻘﺎﯾﺴﻪ ﺷﺪه اﺳﺖ و ﻣﻮﺛﺮ ﺑﻮدن روش ﭘﯿﺸﻨﻬﺎدي از ﻟﺤﺎظ ﺳﺮﻋﺖ ﭘﺎﺳﺦ و ﮐﺎﻫﺶ ﻓﺮاﺟﻬﺶ و ﻓﺮﺟﻬﺶ، ﮐﻨﺘﺮل ﮐﻨﻨﺪه ﮐﻤﺘﺮ)ﭘﯿﭽﯿﺪﮔﯽ ﮐﻤﺘﺮ( ﻧﺸﺎن داده ﺷﺪه اﺳﺖ. ﺷﺒﯿﻪ ﺳﺎزي ﺑﺎ اﺳﺘﻔﺎده از ﻧﺮم اﻓﺰار ﻣﺘﻠﺐ اﻧﺠﺎم ﺷﺪه اﺳﺖ.

Load-frequency control in an isolated microgrid is very important.By turbulence the load, the frequency of the microgrid begins to oscillate which must be damped using the load-frequency control system (secondary control). In this paper, we have reduced the number of utilized controllers for the power supplies in the microgrid (less complexity) and we have incorporated the Model Predictive Controller (MPC), whose weight parameters are optimized using the novel Hybrid Craziness based Particle Swarm Optimization-Pattern Search (HCRPSO-PS) algorithm, for controlling the load-frequency in a two-area microgrid. The results of the proposed controller, in a number of different scenarios, are compared with other methods such as the PID controller optimized with the Social Spider Optimization (SSO) algorithm and the model predictive controller whose weight parameters are obtained using the Craziness based Particle Swarm Optimization (CRPSO) algorithm (to demonstrate the efficiency of the novel hybrid algorithm), considering load variations and the distribution of the sources in the microgrid. We have demonstrated the efficiency of the proposed method in terms of response speed, overshoot/undershoot reduction and control complexity(less complex). Simulations are performed using MATLAB software