A local region enhanced multi-objective fireworks algorithm with subpopulation cooperative selection

Because the fault characteristics of inconsistent fault single battery are not obvious in the electric vehicle battery pack, it is difficult to identify inconsistent fault. Therefore, this paper proposes an inconsistent fault detection method based on a fireworks algorithm (FWA) optimized deep belief network (DBN). The method feeds the raw data signal into a deep belief network algorithm for training, which automatically performs feature extraction and intelligent diagnosis of inconsistencies, without requiring the time domain signal to be periodic. The top-level algorithm of the deep belief network adopts error Back Propagation (BP). Using FWA training to optimize DBN-BP, get the best DBN-BP-FWA model structure.Experimental verification was carried out using real vehicle data from electric vehicles. The inconsistency diagnosis results show that, compared with the traditional inconsistency diagnosis method, the application of this paper's method for electric vehicle single battery fault detection can obtain higher accuracy, with an average accuracy of 96.19%.

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The optimization and operation of multi-energy-coupled microgrids by the improved fireworks algorithm-shuffled frog-leaping algorithm

Yue,

Zhang,

Guo

et al. 2024

PeerJ Computer Science

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This study aims to address optimization and operational challenges in multi-energy coupled microgrids to enhance system stability and reliability. After analyzing the requirements of such systems within comprehensive energy systems, an improved fireworks algorithm (IFWA) is proposed. This algorithm combines an adaptive resource allocation strategy with a community genetic strategy, automatically adjusting explosion range and spark quantity based on individual optimization status to meet actual needs. Additionally, a multi-objective optimization model considering active power network losses and static voltage is constructed, utilizing the shuffled frog-leaping algorithm (SFLA) to solve constrained multi-objective optimization problems. Through simulation experiments on a typical northern comprehensive energy system, conducted with a scheduling period of T = 24, the feasibility and superiority of IFWA-SFLA are validated. Results indicate that IFWA-SFLA performs well in optimizing microgrid stability, managing electrical energy flow effectively within the microgrid, and reducing voltage fluctuations. Furthermore, the circuit structure and control strategy of microgrid energy storage bidirectional inverters based on IFWA are discussed, along with relevant simulation results.

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A local region enhanced multi-objective fireworks algorithm with subpopulation cooperative selection

Cited by 3 publications

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Health risk assessment using chemical signatures of fine and coarse particles collected at breathing level height during firework display in New Delhi, India

Health risk assessment using chemical signatures of fine and coarse particles collected at breathing level height during firework display in New Delhi, India

Fault Detection of Single Cell Battery Inconsistency in Electric Vehicle Based on Fireworks Algorithm Optimized Deep Belief Network

The optimization and operation of multi-energy-coupled microgrids by the improved fireworks algorithm-shuffled frog-leaping algorithm

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