Dynamic leader based collective intelligence for maximum power point tracking of PV systems affected by partial shading condition

Yang, Bo; Yu, Tao; Zhang, Xiaoshun; Li, Haofei; Shu, Hongchun; Sang, Yiyan; Jiang, Lin

doi:10.1016/j.enconman.2018.10.074

Cited by 214 publications

(77 citation statements)

References 49 publications

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“…PSCAD/EMTDC of version 4.5 [36][37][38][39] with sampling rate of 1 MHz is implemented to simulate the faults in the T-connection transmission lines as demonstrated in Figure 1, and its parameters are given in Table 10. The lengths of the T-connection transmission line are set to l 1 = 50 km, l 2 = 40 km, and l 3 = 45 km.…”

Section: Simulation Modelmentioning

confidence: 99%

Fault Model and Travelling Wave Matching Based Single Terminal Fault Location Algorithm for T-Connection Transmission Line: A Yunnan Power Grid Study

Shu

Han

Huang³

et al. 2020

Energies

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Due to the complex structure of the T-connection transmission lines, it is extremely difficult to identify the reflected travelling wave from the fault point and that from the connection point by the measurement from only one terminal. According to the characteristics of the structure of the T-connection transmission line, the reflection of the travelling wave within the line after the failure of different sections in T-connection transmission line are analyzed. Based on the lattice diagram of the travelling wave, the sequence of travelling waves detected at the measuring terminal varies with the fault distance and the faulty section. Moreover, the sequence of travelling waves detected in one terminal is unique at each faulty section. This article calculates the arrival time of travelling waves of fault points at different locations in different sections to form the collection of the travelling wave arrival time sequence. Then the sequence of travelling waves of the new added fault waveforms is extracted to compare with the sequences in the collection for the faulty section identification and fault location. This proposed method can accurately locate the fault with different fault types, fault resistances and system impedances by only single-terminal fault data. Both Power Systems Computer Aided Design/ Electromagnetic Transients including DC (PSCAD/EMTDC) and actual measurement data are implemented to verify the effectiveness of this method.

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Section: Simulation Modelmentioning

confidence: 99%

Fault Model and Travelling Wave Matching Based Single Terminal Fault Location Algorithm for T-Connection Transmission Line: A Yunnan Power Grid Study

Shu

Han

Huang³

et al. 2020

Energies

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“…Generally speaking, the output power of a PV cell is linearly matched with the solar irradiance. If power loss is not considered, and the influence of the ambient temperature is ignored, the output power of a PV unit can be described by [20,21]:…”

Section: Relationship Between Solar Irradiance and Solar Power Outputmentioning

confidence: 99%

Multi-Searcher Optimization for the Optimal Energy Dispatch of Combined Heat and Power-Thermal-Wind-Photovoltaic Systems

Tang

Zhang

et al. 2019

Applied Sciences

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This paper proposes a novel multi-searcher optimization (MSO) algorithm for the optimal energy dispatch (OED) of combined heat and power-thermal-wind-photovoltaic systems. The available power of wind turbine (WT) units and photovoltaic (PV) units is approximated with the probability density functions of wind speed and solar irradiance, respectively. The chaos theory is used to implement a wide global search, which can effectively avoid a low-quality local optimum for OED. Besides, a double-layer searcher is designed to guarantee fast convergence to a high-quality optimal solution. Finally, three benchmark functions and an energy system with 27 units are used for testing the performance of the MSO compared with nine other frequently used heuristic algorithms. The simulation results demonstrate that the proposed technique not only can solve the highly nonlinear, non-smooth, and non-convex OED problem of an energy system, but can also achieve a superior performance for the convergence speed and the optimum quality.

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“…In the past decades, artificial intelligence (AI) [8][9][10][11][12][13][14][15][16][17][18] has been widely used as an effective alternative because of its high independence from an accurate system model and strong global optimization ability. Inspired by nectar gathering of bees in wild nature, the artificial bee colony (ABC) [19] has been applied to optimal distributed generation allocation [8], global maximum power point (GMPP) tracking [20], multi-objective UC [21], and so on, and has the merits of simple structure, high robustness, strong universality, and efficient local search.…”

Section: Introductionmentioning

confidence: 99%

“…However, the ABC mainly depends on a simple collective intelligence without self-learning or knowledge transfer, which is a common weakness of AI algorithms such as genetic algorithm (GA) [9], particle swarm optimization (PSO) [10], group search optimizer (GSO) [11], ant colony system (ACS) [12], interactive teaching-learning optimizer (ITLO) [13], grouped grey wolf optimizer (GGWO) [14], memetic salp swarm algorithm (MSSA) [15], dynamic leader-based collective intelligence (DLCI) [16], and evolutionary algorithms (EA) [17]. Thus, a relatively low search efficiency may result, particularly while considering a new optimization task of a complex industrial system [22], e.g., the optimization of a large-scale power system with different complex new tasks.…”

Section: Introductionmentioning

confidence: 99%

Reactive Power Optimization of Large-Scale Power Systems: A Transfer Bees Optimizer Application

Zhang

Yang

et al. 2019

Processes

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A novel transfer bees optimizer for reactive power optimization in a high-power system was developed in this paper. Q-learning was adopted to construct the learning mode of bees, improving the intelligence of bees through task division and cooperation. Behavior transfer was introduced, and prior knowledge of the source task was used to process the new task according to its similarity to the source task, so as to accelerate the convergence of the transfer bees optimizer. Moreover, the solution space was decomposed into multiple low-dimensional solution spaces via associated state-action chains. The transfer bees optimizer performance of reactive power optimization was assessed, while simulation results showed that the convergence of the proposed algorithm was more stable and faster, and the algorithm was about 4 to 68 times faster than the traditional artificial intelligence algorithms.

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Dynamic leader based collective intelligence for maximum power point tracking of PV systems affected by partial shading condition

Cited by 214 publications

References 49 publications

Fault Model and Travelling Wave Matching Based Single Terminal Fault Location Algorithm for T-Connection Transmission Line: A Yunnan Power Grid Study

Fault Model and Travelling Wave Matching Based Single Terminal Fault Location Algorithm for T-Connection Transmission Line: A Yunnan Power Grid Study

Multi-Searcher Optimization for the Optimal Energy Dispatch of Combined Heat and Power-Thermal-Wind-Photovoltaic Systems

Reactive Power Optimization of Large-Scale Power Systems: A Transfer Bees Optimizer Application

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