Optimization of photovoltaic power system: a comparative study

Дабра, Вишал; Paliwal, K.K.; Sharma, Prashant; Kumar, N.V.A Ravi

doi:10.1186/s41601-017-0036-2

Cited by 50 publications

(31 citation statements)

References 22 publications

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“…The operation of the PV system needs to meet the balance of power supplied and required, and the constraint is shown in (13),…”

Section: Power Balance Constraintsmentioning

confidence: 99%

“…[11] takes the static top and bottom limitation of the grid-connected tie-line transmission power as the constraint of the proposed model, which has a limited effect. In recent years, genetic algorithm, simulated annealing algorithm, particle swarm algorithm [12,13], and other intelligent algorithms have been widely used to solve various optimization problems. The particle swarm optimization algorithm (PSOA) has good accuracy, strong orientation, and is easy to understand and program.…”

Section: Introductionmentioning

confidence: 99%

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Study about Optimization Scheduling Method of Economic Benefit for Grid‐Connected Household Photovoltaic Systems

Ding

2020

IEEJ Transactions Elec Engng

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In order to improve the economic benefit of household photovoltaic (PV) system users under time‐of‐use (TOU) price and enhance their ability to cut peaks and fill valleys for main grid, an optimization method based on dynamic tie‐line transmission power is proposed in this paper, which can be applied to different weather conditions. First, an optimization scheduling model of economic benefit for a grid‐connected PV system is put forward, with the final revenue of user as the objective function. Under the TOU price policy, this model fully considers economic factors such as energy income, energy cost, government subsidy, system maintenance cost, etc. Moreover, the dynamic tie‐line transmission power is taken as the optimization variable, and the improved chaotic simulated annealing particle swarm optimization algorithm is used to solve the optimization problem. Finally, a typical household grid‐connected PV system is taken as study case, and convincing results are obtained to verify the proposed model and optimal strategy. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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“…The operation of the PV system needs to meet the balance of power supplied and required, and the constraint is shown in (13),…”

Section: Power Balance Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Study about Optimization Scheduling Method of Economic Benefit for Grid‐Connected Household Photovoltaic Systems

Ding

2020

IEEJ Transactions Elec Engng

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“…Photovolatic (PV) energy has received much attention, due to its many advantages; it is abundant, inexhaustible and clean [180]. However, due to the chaotic and erratic nature of the weather systems, the power output of PV energy systems is intermittent, volatile and random [181].…”

Section: F Power Systemsmentioning

confidence: 99%

A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends

Sengupta¹,

Basak²,

Saikia³

et al. 2019

Preprint

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Deep learning has taken over - both in problems beyond the realm of traditional, hand-crafted machine learning paradigms as well as in capturing the imagination of the practitioner sitting on top of petabytes of data. While the public perception about the efficacy of deep neural architectures in complex pattern recognition tasks grows, sequentially up-to-date primers on the current state of affairs must follow. In this review, we seek to present a refresher of the many different stacked, connectionist networks that make up the deep learning architectures followed by automatic architecture optimization protocols using multi-agent approaches. Further, since guaranteeing system uptime is fast becoming an indispensable asset across multiple industrial modalities, we include an investigative section on testing neural networks for fault detection and subsequent mitigation. This is followed by an exploratory survey of several application areas where deep learning has emerged as a game-changing technology - be it anomalous behavior detection in financial applications or financial time-series forecasting, predictive and prescriptive analytics, medical imaging, natural language processing or power systems research. The thrust of this review is on outlining emerging areas of application-oriented research within the deep learning community as well as to provide a handy reference to researchers seeking to embrace deep learning in their work for what it is: statistical pattern recognizers with unparalleled hierarchical structure learning capacity with the ability to scale with information.

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“…Dev.) [42][43][44] were introduced in order to assess the stability. One can easily find that the convergence rate of the TBO was faster compared with that of other algorithms, as illustrated in Figure 10.…”

Section: Study Of Behavior Transfermentioning

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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Optimization of photovoltaic power system: a comparative study

Cited by 50 publications

References 22 publications

Study about Optimization Scheduling Method of Economic Benefit for Grid‐Connected Household Photovoltaic Systems

Study about Optimization Scheduling Method of Economic Benefit for Grid‐Connected Household Photovoltaic Systems

A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends

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

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