A Compact Pigeon-Inspired Optimization for Maximum Short-Term Generation Mode in Cascade Hydroelectric Power Station

Tian, Ai-Qing; Chu, Shu-Chuan; Pan, Jeng‐Shyang; Cui, Huanqing; Zheng, Weimin

doi:10.3390/su12030767

Cited by 77 publications

(39 citation statements)

References 36 publications

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“…The Pigeon-inspired optimization algorithm is a new meta-heuristic algorithm. PIO was proposed in 2014 and was inspired by the behavior of pigeons returning home [48]. Pigeons can find their home with tools that help them to return home, the tools include magnetic fields, the sun, and landmarks.…”

Section: Pigeon-inspired Optimizationmentioning

confidence: 99%

A Novel Pigeon-Inspired Optimization Based MPPT Technique for PV Systems

et al. 2020

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The conventional maximum power point tracking (MPPT) method fails in partially shaded conditions, because multiple peaks may appear on the power–voltage characteristic curve. The Pigeon-Inspired Optimization (PIO) algorithm is a new type of meta-heuristic algorithm. Aiming at this situation, this paper proposes a new type of algorithm that combines a new pigeon population algorithm named Parallel and Compact Pigeon-Inspired Optimization (PCPIO) with MPPT, which can solve the problem that MPPT cannot reach the near global maximum power point. This hybrid algorithm is fast, stable, and capable of globally optimizing the maximum power point tracking algorithm. Therefore, the purpose of this article is to study the performance of two optimization techniques. The two algorithms are Particle Swarm Algorithm (PSO) and improved pigeon algorithm. This paper first studies the mechanism of multi-peak output characteristics of photovoltaic arrays in complex environments, and then proposes a multi-peak MPPT algorithm based on a combination of an improved pigeon population algorithm and an incremental conductivity method. The improved pigeon algorithm is used to quickly locate near the maximum power point, and then the variable step size incremental method INC (incremental conductance) is used to accurately locate the maximum power point. A simulation was performed on Matlab/Simulink platform. The results prove that the method can achieve fast and accurate optimization under complex environmental conditions, effectively reduce power oscillations, enhance system stability, and achieve better control results.

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Section: Pigeon-inspired Optimizationmentioning

confidence: 99%

A Novel Pigeon-Inspired Optimization Based MPPT Technique for PV Systems

et al. 2020

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“…In [17], a novel adaptation of the multi-group quasi-affine transformation evolutionary algorithm for global optimization was proposed. In [18], a compact pigeon-inspired optimization algorithm was proposed to solve complex scientific and industrial problems with many data packets, including the use of classical optimization problems and the ability to find optimal solutions in many solution spaces with limited hardware resources. Those studies provide a feasible solution to the problem under acceptable computational time and space, and the solution cannot be predicted in advance [19].…”

Section: 'Pso-infotaxis' Algorithm-based Exploration Of Cooperative Usvsmentioning

confidence: 99%

“…P t g = argmax P sh t r j (18) Here, P t r j is the probability of source position estimated in USV i's t-th iteration, and P sh t r j is the sharing probability of source position estimated in the multiple USVs' t-th iteration.…”

Section: 'Infotaxis' Algorithm Of Multi-usv Exploration Based On Imprmentioning

confidence: 99%

Improved ‘Infotaxis’ Algorithm-Based Cooperative Multi-USV Pollution Source Search Approach in Lake Water Environment

Huang

2020

Symmetry

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This paper studies the cooperation method of multi-cooperative Unmanned Surface Vehicles (USVs) for chemical pollution source monitoring in a dynamic water environment. Multiple USVs formed a mobile sensor network in a symmetrical or asymmetrical formation. Based on ‘Infotaxis’ algorithms for multi-USV, an improved shared probability is proposed for solving the problems of low success rate and low efficiency resulting from the cognitive differences of multi-USV in cooperative exploration. By introducing the confidence factor, the cognitive differences between USVs are coordinated. The success rate and the efficiency of exploration are improved. To further optimize the exploration strategy, the particle swarm optimization (PSO) algorithm is introduced into the ‘Infotaxis’ algorithm to plan the USVs’ exploration path. This method is called the ‘PSO-Infotaxis’ algorithm. The effectiveness of the proposed method is verified by simulation and laboratory experiments. A comparison of the test results shows that the ‘PSO-Infotaxis’ algorithm is superior with respect to exploring efficiency. It can reduce the uncertainty of the estimation for source location faster and has lower exploration time, which is most important for the exploration of a large range of water areas.

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“…Intelligent computing has been paid more and more attention by researchers because of its excellent performance in solving optimization problems, such as Artificial Bee Colony (ABC) [8][9][10], Particle Swarm Optimization (PSO) [11][12][13][14], Genetic Algorithm (GA) [15], Ant Colony Optimization (ACO) [16][17][18], Cat Swarm Optimization (CSO) [19][20][21], Difference Evolution (DE) [22][23][24], Multi-Verse Optimizer [25,26], Symbiotic Organism Search Algorithm [27,28], QUATRE [29][30][31] and et al To improve the optimization efficiency, many methods have been put forward. For example, a compact method is implemented to achieve better performance based on the memory of a single individual [32][33][34][35][36][37]. The simulation of the benchmark function can also effectively improve the speed and sear-ability of the original algorithm [38].…”

Section: Introductionmentioning

confidence: 99%

3-D Terrain Node Coverage of Wireless Sensor Network Using Enhanced Black Hole Algorithm

Pan

Chai

Chu

et al. 2020

Sensors

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In this paper, a new intelligent computing algorithm named Enhanced Black Hole (EBH) is proposed to which the mutation operation and weight factor are applied. In EBH, several elites are taken as role models instead of only one in the original Black Hole (BH) algorithm. The performance of the EBH algorithm is verified by the CEC 2013 test suit, and shows better results than the original BH. In addition, the EBH and other celebrated algorithms can be used to solve node coverage problems of Wireless Sensor Network (WSN) in 3-D terrain with satisfactory performance.

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A Compact Pigeon-Inspired Optimization for Maximum Short-Term Generation Mode in Cascade Hydroelectric Power Station

Cited by 77 publications

References 36 publications

A Novel Pigeon-Inspired Optimization Based MPPT Technique for PV Systems

A Novel Pigeon-Inspired Optimization Based MPPT Technique for PV Systems

Improved ‘Infotaxis’ Algorithm-Based Cooperative Multi-USV Pollution Source Search Approach in Lake Water Environment

3-D Terrain Node Coverage of Wireless Sensor Network Using Enhanced Black Hole Algorithm

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