Application of flower pollination algorithm to parameter identification of DC motor model

Puangdownreong, Deacha; Hlungnamtip, S.; Thammarat, C.; Nawikavatan, Auttarat

doi:10.1109/ieecon.2017.8075889

Cited by 19 publications

(13 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Flower Pollination Algorithm (FPA) is a meta-heuristic method originally proposed in [44] that mimics the reproductive process in flowering plants. It is a powerful optimization tool applied with good results to non-linear, non-convex, and MINLP problems [29,39,40]. The distinguishing features of FPA are the reduced number of parameters and the strategy of alternating between local and global search.…”

Section: The Flower Pollination Algorithmmentioning

confidence: 99%

“…The CBs placement is carried out using FPA, and then the limited search determines the optimal CBs capacities, working in a reduced set of buses defined in the first stage. As FPA is built under a single strategic parameter p, it can provide excellent convergence properties with a small population and few iterations [39,40]. By combining both techniques, the prohibitive computing time of ES becomes viable, allowing the method to yield feasible and high-quality solutions in a robust and effective way.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

FPAES: A Hybrid Approach for the Optimal Placement and Sizing of Reactive Compensation in Distribution Grids

2020

View full text Add to dashboard Cite

Reactive power compensation with Capacitor Banks (CBs) is one of the most successful approaches used in distribution systems, mainly due to their versatility, long-term acceptance in the power industry, and reduced costs. Most allocation methods, however, lack specific strategies to handle the limited discrete nature of CBs sizes seeking to improve the overall optimization and computational performance. We present an algorithm for the Optimal Placement of Capacitor Banks (OPCB) in distribution systems by means of a hybrid Flower Pollination Algorithm (FPA)–Exhaustive Search (ES) approach. The pollination process itself determines the sets of buses for placement, while CBs sizes and the final fitness values of each pollen are selected after a full-search is conducted in the sizing space. As the sizing phase works on the limited search space of predetermined discrete bank values, the computational effort to find the optimum CB capacity is greatly reduced. Tests were performed on distribution systems of 10, 34, and 85 buses with respect to the objective function, final losses, and voltage profile. The algorithm offers an excellent compromise between solution quality and computational effort, when compared to similar approaches.

show abstract

Section: The Flower Pollination Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

FPAES: A Hybrid Approach for the Optimal Placement and Sizing of Reactive Compensation in Distribution Grids

2020

View full text Add to dashboard Cite

show abstract

“…Several studies on DC motor control are divided into two concepts, namely: conventional methods and artificial intelligence methods. several artificial intelligence concepts for controlling such as: Whale optimization algorithm [8], [9], Harris Hawks optimization algorithm [10], flower pollination algorithm [11], firefly algorithm [12], ant colony algorithm [13], [14], and neural network [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Improving neural network using a sine tree-seed algorithm for tuning motor DC

Aribowo

Suprianto

Joko

2021

IJPEDS

View full text Add to dashboard Cite

A DC motor is applied to delicate speed and position in the industry. The stability and productivity of a system are keys for tuning of a DC motor speed. Stabilized speed is influenced by load sway and environmental factors. In this paper, a comparison study in diverse techniques to tune the speed of the DC motor with parameter uncertainties is showed. The research has discussed the application of the feed-forward neural network (FFNN) which is enhanced by a sine tree-seed algorithm (STSA). STSA is a hybrid method of the tree-seed algorithm (TSA) and Sine Cosine algorithm. The STSA method is aimed to improve TSA performance based on the sine cosine algorithm (SCA) method. A feed-forward neural network (FFNN) is popular and capable of nonlinear issues. The focus of the research is on the achievement speed of DC motor. In addition, the proposed method will be compared with proportional integral derivative (PID), FFNN, marine predator algorithm-feed-forward neural network (MPA-NN) and atom search algorithm-feed-forward neural network (ASO-NN). The performance of the speed from the proposed method has the best result. The settling time value of the proposed method is more stable than the PID method. The ITAE value of the STSA-NN method was 31.3% better than the PID method. Meanwhile, the ITSE value is 29.2% better than the PID method.

show abstract

“…The performance tests of the FPA against several benchmark functions were reported [29], [30]. Also, the FPA was successfully conducted to optimize many real-world engineering problems [37] and linear antenna array optimization [38]), civil engineering system optimization (frames and truss systems [39] and structure engineering design [40]), image processing optimization [41], transportation optimization (TSP [42]), control system optimization (control system design [43][44][45] and model identification [46]) and hybrid renewable energy saving optimization [47]. Readers can find the state-of-the-art developments and significant applications of the FPA in [48], [49].…”

Section: Introductionmentioning

confidence: 99%

Solving Multiple Vehicle Routing Problems with Time Constraintsby Flower Pollination Algorithm Optimization

Suwannarongsri

2020

Wseas Transactions on Systems

View full text Add to dashboard Cite

The multiple vehicle routing problem (MVRP) with the time constraint is one of the most importantreal-world problems in industrial and logistic engineering. The MVRP problems can be considered as a class ofthe non-polynomial (NP) time-complete combinatorial optimization problem. Such the MVRP problems aim tofind the set of routes with the shortest total distance for overall minimum route cost serving all the givendemands by the fleet of vehicles. Based on modern optimization, the MVRP problems can be optimally solvedby the potential metaheuristic optimization techniques. The flower pollination algorithm (FPA) is one of themost efficient metaheuristic optimizers proposed for solving the combinatorial optimization problems. Withfew searching parameters, the algorithm of the FPA is not complex and ease of use. In this paper, the FPA isapplied to solve five selected benchmark MVRP problems with the time constraints consisting of 50-100destinations. Results obtained by the FPA will be compared with those obtained by genetic algorithm (GA), tabusearch (TS) and particle swarm optimization (PSO). From results, the FPA can provide optimal solutions of allfive selected problems. Optimal results obtained by the FPA are superior to PSO, TS and GA, respectively,with shorter total distance and computational time consumed

show abstract

Application of flower pollination algorithm to parameter identification of DC motor model

Cited by 19 publications

References 6 publications

FPAES: A Hybrid Approach for the Optimal Placement and Sizing of Reactive Compensation in Distribution Grids

FPAES: A Hybrid Approach for the Optimal Placement and Sizing of Reactive Compensation in Distribution Grids

Improving neural network using a sine tree-seed algorithm for tuning motor DC

Solving Multiple Vehicle Routing Problems with Time Constraintsby Flower Pollination Algorithm Optimization

Contact Info

Product

Resources

About