Dynamic arithmetic optimization algorithm control of distributed generations for demand balancing and enhancing power quality of unbalanced distribution systems
Abstract Unbalanced power systems cause transformers and generators to overheat, system losses to climb, and protective devices to trigger. An optimization-based control technique for distributed generators (DG) balances demand and improves power quality in three imbalanced distribution systems with...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-80432-z |
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| Summary: | Abstract Unbalanced power systems cause transformers and generators to overheat, system losses to climb, and protective devices to trigger. An optimization-based control technique for distributed generators (DG) balances demand and improves power quality in three imbalanced distribution systems with 10, 13, and 37 nodes. Each system phase has its own DG. Particle Swarm Optimization (PSO) and Dynamic Arithmetic Optimization Algorithm (DAOA) determine each phase’s best locations, sizes, and power factors. The PSO and DAOA algorithms optimize the three imbalanced distribution systems at full load and throughout the day. The three DG sources are at the same node for easy operation, maintenance, and control. Each system’s voltage, power, and current imbalance factors (VUF, PUF, CUF) are determined according to ANSI and IEEE standards. Optimization techniques lower VUF to meet the criteria for all studied systems. PUF values drop from 116%, 28%, and 17% to virtually zero for the 10-, 13-, and 37-bus systems, while CUF improves similarly. Power losses are minimized by 80%, 51%, and 52% for each system. The voltage profile improves, reducing voltage variance across all three systems. |
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| ISSN: | 2045-2322 |