ﮐﻨﺘﺮل ﻏﯿﺮﻣﺘﻤﺮﮐﺰ ﻣﺒﺪل ﻫﺎي دوﻃﺮﻓﻪ در ﯾﮏ رﯾﺰﺷﺒﮑﻪ DC ﻣﺘﺼﻞ ﺑﻪ ﺷﺒﮑﻪ ﺑﻪ ﻣﻨﻈﻮر اﻓﺰاﯾﺶ ﭘﺎﯾﺪاري ﺳﯿﺴﺘﻢ ﺑﻪ ﮐﻤﮏ اﻟﮕﻮر ﯾﺘﻢ ژﻧﺘﯿﮏ - ﻋﺼﺒﯽ

Decentralized Control of Bidirectional Converters in a Grid-connected DC Microgrid to Increase System Stability Using a Genetic-Neural Algorithm

از آﻧﺠﺎ ﮐﻪ ﻣﺒﺪلﻫﺎي ﻗﺪرت دوﻃﺮﻓﻪ ﻗﺎﺑﻠﯿﺖ ﻋﺒﻮر از ﺧﻄﺎ را دارﻧﺪ، ﺑﻪﮐﺎرﮔﯿﺮي آﻧﻬﺎ ﺑﺎﻋﺚ ﺑﻬﺒﻮد ﻗﺎﺑﻠﯿﺖ اﻃﻤﯿﻨﺎن ﺳﯿﺴﺘﻢ ﻣﯽﺷﻮد. ﺑﻨﺎﺑﺮاﯾﻦ، ﺳﺎﺧﺘﺎر ﻣﺒﺪلﻫﺎي ﻗﺪرت دوﻃﺮﻓﻪ ﻣﻮازي، ﺑﺮاي رﯾﺰﺷﺒﮑﻪ DC ﻣﺘﺼﻞ ﺑﻪ ﺷﺒﮑﻪ اﺗﺨﺎذ ﺷﺪه اﺳﺖ. ﻣﯿﺎن ﻣﺒﺪلﻫﺎي ﻗﺪرت دوﻃﺮﻓﻪ ﻣﻮازي، ﺟﺮﯾﺎن ﮔﺮدﺷﯽ وﺟﻮد دارد ﮐﻪ ﻇﺮﻓ ﯿﺖ ﮐﻠﯽ ﺳﯿﺴﺘﻢ را ﻣﺤﺪود ﻣﯽﮐﻨﺪ و ﻣﯽﺗﻮاﻧﺪ ﺑﻪ دﺳﺘﮕﺎه ﻫﺎي ﺳﻮﺋﯿﭽﯿﻨﮓ آﺳﯿﺐ ﺑﺮﺳﺎﻧﺪ. ﺑﺮاي ﺣﻞ اﯾﻦ ﻣﺴﺌﻠﻪ، در اﯾﻦ ﻣﻘﺎﻟﻪ ﻣﮑﺎﻧﯿﺰم ﺗﻮﻟﯿﺪ ﺟﺮﯾﺎن ﮔﺮدﺷﯽ در ﺳﻤﺖ AC، ﻫﻨﮕﺎﻣ ﯽ ﮐﻪ ﭼﻨﺪﯾﻦ ﻣﺒﺪل ﻗﺪرت دوﻃﺮﻓﻪ ﺑﻪﺻﻮرت ﻣﻮازي ﺑﺎ ﻫﻢ ﮐﺎر ﻣﯽﮐﻨﻨﺪ، ﺑﺎ در ﻧﻈﺮ ﮔﺮﻓﺘﻦ ﺗﺄﺛﯿﺮ ﺑﺎرﻫﺎي ﺗﻮان ﺛﺎﺑﺖ ﺗﻮﺳﻂ اﺳﺘﺮاﺗﮋ ي دروپ Pdc-vdc2-f ﻣﻮرد ﺗﻮﺟﻪ ﻗﺮار ﮔﺮﻓﺖ ﺗﺎ اﺷﺘﺮاكﮔﺬاري ﺗﻮانDC و اﻧﻄﺒﺎقﭘﺬﯾﺮي ﺑﺎ ﻓﺮﮐﺎﻧﺲ ﺷﺒﮑﻪ اﻧﺠﺎم ﺷﻮد. ﺿﺮاﯾﺐ PI ﮐﻨﺘﺮﻟﺮ ﻣﺒﺪلﻫﺎي ﻗﺪرت ﺗﻮﺳﻂ اﻟﮕﻮرﯾﺘﻢ ژﻧﺘﯿﮏ آﻧﻼﯾﻦ و ﺷﺒﮑﻪ ﻋﺼﺒﯽ ﺑﻬﯿﻨﻪ ﺷﺪ و ﺑﺎ ﺷﺮاﯾﻂ ﺳﯿﺴﺘﻢ ﺑﻪروزرﺳﺎﻧﯽ ﮔﺮدﯾﺪ. ﻫﺮ دو راﺑﻄﻪ ﭘ ﺎﯾﺪار و دﯾﻨﺎﻣﯿﮑﯽ ﺑﯿﻦ Pdc و vdc2 ﺧﻄ ﯽ ﻫﺴﺘﻨﺪ و ﺑﺎرﻫﺎي ﺗﻮان ﺛﺎﺑﺖ ﻗﻄﺐ ﻫﺎي ﻧﺎﭘﺎﯾ ﺪار را ﺑﺮا ي ﺳﯿﺴﺘﻢ اﯾﺠﺎد ﻧﻤﯽﮐﻨﻨﺪ. ﻧﺘﺎﯾﺞ ﻣﻘﺎﯾﺴﻪ ﺷﺪه ﺑﺎ ﮐﻨﺘﺮل دروپ idc-vdc ﻣﺘﺪاول ﻧﺸﺎن ﻣﯽدﻫﺪ اﺳﺘﺮاﺗﮋي ﭘﯿﺸﻨﻬﺎدي Pdc-vdc2-f ﻣﯽﺗﻮاﻧﺪ ﮐﻨﺘﺮل ﺗﻮان dc و ﭘﺎﯾﺪاري ﺳﯿﺴﺘﻢ را اﻓﺰاﯾﺶ دﻫﺪ ﮐﻪ ﺑﺎﻋﺚ اﻓﺰاﯾﺶ دﯾﻨﺎﻣﯿﮏ ﺗﻨﻈﯿﻢ وﻟﺘﺎژ dc ﻣﯽ ﺷﻮد.

Using bidirectional parallel converters improves system reliability as they can bypass faults. Therefore, the bidirectional parallel converters structure is adopted for the DC microgrid connected to the grid. There is a circulating current between the bidirectional parallel converters that limits the overall capacity of the system and can damage switching devices. To solve this problem, in this paper, the mechanism of generating circulating current on the AC side when several bidirectional parallel converters work together was taken into consideration with the effect of constant power loads by the droop strategy Pdc-vdc2-f to share DC power and network frequency adaptation. The PI coefficients of the bidirectional parallel converters controller were optimized by online genetic algorithm and neural networks and updated with system conditions. Both the stable and dynamic relationships between Pdc and vdc2 are linear and did not create constant power loads of unstable poles for the system. The results indicated that compared with conventional idc-vdc droop control, the proposed Pdc-vdc2-f strategy can increase dc power control and system stability, consequently increasing the dc voltage regulation dynamics.