Distance-Based Intelligent Particle Swarm Optimization for Optimal Design of Permanent Magnet Synchronous Machine

Lee, Jin Hwan; Kim, Jong-Wook; Song, Jun-Young; Kim, Dae Woo; Kim, Yong-Jae

doi:10.1109/tmag.2017.2658027

Cited by 35 publications

(16 citation statements)

References 9 publications

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“…Since the cost J i (k) is complex and non-differentiable, using the traditional search techniques is difficult to solve the optimization problem. In the works of Lee et al 29 and Hojjati et al, 30 the population-based optimization approaches provide good solutions to similar complicated problems. Inspired by the facts, the DE algorithm is utilized to solve the local optimization, then the control law u i can be worked out directly.…”

Section: Receding Optimizationmentioning

confidence: 99%

Adaptive model predictive control with extended state observer for multi‐UAV formation flight

Zhang

Sun

Deng

2020

Adaptive Control & Signal

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This article studies the adaptive model predictive control with extended state observers (ESO) to deal with multiple unmanned aerial vehicles formation flight in presence of external disturbances and system uncertainties. Specifically, to deal with the mismatch of predictive model caused by external disturbances and system uncertainties, ESOs are introduced to estimate the lumped disturbances, where the ultimately bounded property of observer system can be guaranteed by using the Lyapunov stability theorem. With these observations, the distributed adaptive model predictive controller is designed to achieve trajectory tracking and disturbance rejection simultaneously for multiple unmanned aerial vehicles, as well as taking the state and input saturation into account. Moreover, the stability of proposed model predictive controller is analyzed. Finally, the simulation examples are provided to illustrate the validity of the proposed control scheme.

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Section: Receding Optimizationmentioning

confidence: 99%

Adaptive model predictive control with extended state observer for multi‐UAV formation flight

Zhang

Sun

Deng

2020

Adaptive Control & Signal

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“…PSO is an algorithm widely used in power system problems because of its simple implementation, it converges to the optimal solution in several problems where most analytical approaches fail to converge and is more effective in preserving the variety of the swarm since all particles use the information related to the most successful particle in order to improve them . Due to these features and other features, other techniques of PSO are introduced such as Selective PSO (SPSO) algorithm, Improved PSO Based on Success Rate (IPSO‐SR), Distance‐based Intelligent PSO (DbIPSO), a Modified PSO (MPSO) algorithm, MultiObjective PSO (MOPSO), and Binary PSO (BPSO) . These techniques are used for finding the optimal DG allocation.…”

Section: Opso Implementationmentioning

confidence: 99%

Assessment of optimal allocation of renewable distributed generator sources in distribution network

Nasef

Osheba

Khattab

et al. 2020

Engineering Reports

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The integration of distributed generators (DGs) into distribution networks in optimal allocation is one of the main issues facing power system engineers to ensure improved stability and economic operation. This article presents a detailed analysis of the impacts of the optimal allocation and the number of DGs on both system steady-state and transient performances of distribution networks. An oscillatory particle swarm optimization (OPSO) algorithm was used to find the optimal allocations of DGs via minimizing various objective functions that deal with Total Transmission Losses, Voltage Regulation, and Power Performance Index. The OPSO is used to optimize these functions as a single and as a multiobjective optimization problem. The effectiveness of the method is demonstrated with the IEEE-14 bus as an example of distribution networks with a 50% increase in system loading. Two penetration scenarios have been considered, the optimal sizes and locations of DGs are obtained, and the results are presented.In addition, the impact of the penetration level of Photovoltaic and Wind Energy sources on transient performance is obtained using detailed nonlinear models of both synchronous machines and DGs sources. The system response is then obtained when the system is subjected to a three-phase short circuit fault for six cycles and the results are presented in a comparative form for different penetration levels. The techniques and results presented in the article form a useful base for power system engineers in planning and operating distribution systems with high penetration levels of RDG sources. K E Y W O R D Sconcentrated and distributed DG (C-DG, D-DG), distributed generation (DG), optimal DG allocations, oscillatory PSO, penetration level.

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“…PSO uses groups of individuals (called particles) to perform searches as with evolutionary algorithms, and particles can be updated from each iteration to the other [26][27][28][29][30]. In order to find the optimal solution, each particle changes its search direction based on two factors: its best previous location (p best ) and all other members' best locations (g best ) [31][32][33][34]. Shi et al called p best the cognitive part and g best the social part [35].…”

Section: Introductionmentioning

confidence: 99%

Incorporating Particle Swarm Optimization into Improved Bacterial Foraging Optimization Algorithm Applied to Classify Imbalanced Data

Lee

et al. 2020

Symmetry

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In this paper, particle swarm optimization is incorporated into an improved bacterial foraging optimization algorithm, which is applied to classifying imbalanced data to solve the problem of how original bacterial foraging optimization easily falls into local optimization. In this study, the borderline synthetic minority oversampling technique (Borderline-SMOTE) and Tomek link are used to pre-process imbalanced data. Then, the proposed algorithm is used to classify the imbalanced data. In the proposed algorithm, firstly, the chemotaxis process is improved. The particle swarm optimization (PSO) algorithm is used to search first and then treat the result as bacteria, improving the global searching ability of bacterial foraging optimization (BFO). Secondly, the reproduction operation is improved and the selection standard of survival of the cost is improved. Finally, we improve elimination and dispersal operation, and the population evolution factor is introduced to prevent the population from stagnating and falling into a local optimum. In this paper, three data sets are used to test the performance of the proposed algorithm. The simulation results show that the classification accuracy of the proposed algorithm is better than the existing approaches.

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Distance-Based Intelligent Particle Swarm Optimization for Optimal Design of Permanent Magnet Synchronous Machine

Cited by 35 publications

References 9 publications

Adaptive model predictive control with extended state observer for multi‐UAV formation flight

Adaptive model predictive control with extended state observer for multi‐UAV formation flight

Assessment of optimal allocation of renewable distributed generator sources in distribution network

Incorporating Particle Swarm Optimization into Improved Bacterial Foraging Optimization Algorithm Applied to Classify Imbalanced Data

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