Electric spring (ES)， as a new technology for demand-side response， has become a hot issue in the power industry. In order to fully exploit the potential of ES， this paper proposes a multi-objective power quality optimization control strategy for novel ES on the basis of AFSA to optimize the voltage of critical load (CL)， system frequency and power factor of non-critical load (NCL). Firstly， a new back-to-back electric spring (B2B-ES) topology is proposed to realize bidirectional flow of power on the load side. Secondly， an improved artificial fish swarm algorithm (AFSA) is proposed， which is based on the weighted average visual range and adaptive crowding factor. The multi-objective optimization function is designed to extract the local value of the load-side NCL voltage and system frequency to optimize the ES control signal， so as to compensate the power quality of the system online by adjusting the power of the electric spring. Finally， the simulation proves that the proposed optimization control can overcome the shortcomings of single-objective and poor real-time performance of the existing ES control method， and realize real-time optimization of multi-objective power quality control.
Key words
electric spring (ES) /
back-to-back (B2B) structure /
multi-objective power quality optimization /
artificial fish swarm algorithm (AFSA )
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References
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Funding
This work is supported by National Natural Science Foundation of China（No.61275038）.
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