Optimizing the wind power generation in low wind speed areas using an advanced hybrid RBF neural network coupled with the HGA-GSA optimization method

Assareh, Ehsanolah; Poultangari, Iman; Tandis, Emad; Nedaei, Mojtaba

doi:10.1007/s12206-016-0945-4

Cited by 8 publications

(5 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on this, Chen et al (2023) improved the algorithm and proposed a short-term wind power prediction model based on VMD-GRU to improve the utilization rate of renewable wind resources; meanwhile, all of the literature studies also suggest that it is not possible to fully utilize the low wind speed. Assareh et al (2016) used an advanced optimization model to regulate the torque of wind turbines to improve the utilization of wind energy in lowwind speed areas.…”

Section: Literature Reviewmentioning

confidence: 99%

Integrated energy system scheduling optimization considering vertical-axis wind turbines and thermal inertia in oilfield management areas

Wang,

Cui,

Ren

et al. 2024

Front. Energy Res.

View full text Add to dashboard Cite

Traditional horizontal-axis wind turbines (HAWTs) have limited efficiency in low-wind speed regions. In this paper, an integrated energy system (IES) incorporating vertical-axis wind turbines (VAWTs) is proposed; this IES is located in an oilfield management area, which can utilize the low-wind speed resources more efficiently and improve renewable energy consumption, and it also introduces a demand response model based on thermal inertia (TI), thus smoothing out the volatility caused by the VAWT. Typical output scenarios are obtained through stochastic optimization to deal with wind turbine and photovoltaic output uncertainties, and an optimal scheduling model is proposed to minimize the system operating cost. Finally, a simulation study was conducted in a micro-oilfield management area in Shandong Province, China, to demonstrate the performance of the proposed system. The results show that the IES using a VAWT and TI can increase the renewable energy consumption capacity by 87% over the conventional HAWT system, change the user behavior, increase the economic efficiency by 12%, and achieve the smoothing of load-side fluctuation of electric and thermal loads, peak shaving, and valley filling. This paper provides a feasible solution for an IES in low-wind speed areas.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Integrated energy system scheduling optimization considering vertical-axis wind turbines and thermal inertia in oilfield management areas

Wang,

Cui,

Ren

et al. 2024

Front. Energy Res.

View full text Add to dashboard Cite

show abstract

“…By equating the denominator of the closed-loop transfer function of Equation (13) under the general form of a second order system, the parameters of PI rotor current controllers can be found as follows [8,15]:…”

Section: Control Of the Rscmentioning

confidence: 99%

“…In addition, an indirect power control strategy was achieved using GA-tuned integral sliding mode controllers, where the GA was used to tune the parameters of the direct and quadrature rotor current controllers [12]. Moreover, Assareh et al [13] proposed a hybrid GA along with a gravitational search algorithm to attain the optimal gains of the PI controller for the torque regulation of a DFIG-based WT system. In all of these studies, the optimization algorithms are applied to obtain only the PI controllers' gains of the active and reactive powers at the RSC without discussion about the DC-link voltage regulation loop at the GSC and their effect on the overall control system.…”

Section: Introductionmentioning

confidence: 99%

Direct Power Control Optimization for Doubly Fed Induction Generator Based Wind Turbine Systems

Alhato

2019

MCA

View full text Add to dashboard Cite

This study presents an intelligent metaheuristics-based design procedure for the Proportional-Integral (PI) controllers tuning in the direct power control scheme for 1.5 MW Doubly Fed Induction Generator (DFIG) based Wind Turbine (WT) systems. The PI controllers’ gains tuning is formulated as a constrained optimization problem under nonlinear and non-smooth operational constraints. Such a formulated tuning problem is efficiently solved by means of the proposed Thermal Exchange Optimization (TEO) algorithm. To evaluate the effectiveness of the introduced TEO metaheuristic, an empirical comparison study with the homologous particle swarm optimization, genetic algorithm, harmony search algorithm, water cycle algorithm, and grasshopper optimization algorithm is achieved. The proposed TEO algorithm is ensured to perform several desired operational characteristics of DFIG for the active/reactive power and DC-link voltage simultaneously. This is performed by solving a multi‐objective function optimization problem through a weighted‐sum approach. The proposed control strategy is investigated in MATLAB/environment and the results proved the capabilities of the proposed control system in tracking and control under different scenarios. Moreover, a statistical analysis using non-parametric Friedman and Bonferroni–Dunn’s tests demonstrates that the TEO algorithm gives very competitive results in solving global optimization problems in comparison to the other reported metaheuristic algorithms.

show abstract

“…With continuous improvement and development, the RBF network has been widely used in various applications of the power system [24][25][26][27][28][29][30]. However, the RBF network has certain problems-the network structure, particularly the number of hidden layer nodes, is difficult to determine and the training process easily falls into the local minima that affects its practical application [31][32][33][34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Rotor Transverse Slots’ Influence on Negative Sequence Loss and Heat Distribution Prediction of Large Generators Based on Improved Radial Basis Function Process Neural Network

Guo

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Owing to the advantages of scientific computation and the data feature support provided by artificial intelligence technology, the theoretical exploration and application research of new computing methods for large generators, one of the most expensive energy equipment in a power system, has become a research hotspot toward solving the bearing limit and operation capacity under abnormal working conditions. Because of many factors affecting the distribution of negative sequence loss and temperature rise that have an extremely complex nonlinear relationship, the traditional calculation and prediction methods of negative sequence conditions cannot suitably reflect the time accumulation effect. Therefore, a prediction method of rotor steady-state negative sequence heating based on radial basis function process neural network is proposed in this paper, and a negative sequence working condition prediction model is established. Accordingly, this study focuses on the study of the relationship among negative sequence heating characteristics, negative sequence component proportion, and transverse slot; additionally, their influence degree, variation relationship, and main principles are further explored that provide a theoretical basis for the design and operation of large generators. As observed from the test results, the steady-state negative sequence condition prediction method based on the improved genetic algorithm radial basis function process neural network features high accuracy; it is a feasible prediction method, specifically for negative sequence conditions of large generators.

show abstract

Optimizing the wind power generation in low wind speed areas using an advanced hybrid RBF neural network coupled with the HGA-GSA optimization method

Cited by 8 publications

References 16 publications

Integrated energy system scheduling optimization considering vertical-axis wind turbines and thermal inertia in oilfield management areas

Integrated energy system scheduling optimization considering vertical-axis wind turbines and thermal inertia in oilfield management areas

Direct Power Control Optimization for Doubly Fed Induction Generator Based Wind Turbine Systems

Rotor Transverse Slots’ Influence on Negative Sequence Loss and Heat Distribution Prediction of Large Generators Based on Improved Radial Basis Function Process Neural Network

Contact Info

Product

Resources

About