Global Renewable Energy-Based Electricity Generation and Smart Grid System for Energy Security

Islam, Mohammad Aminul; Hasanuzzaman, M.; Rahim, N.A.; Nahar, Afroza; Hosenuzzaman, M.

doi:10.1155/2014/197136

Cited by 79 publications

(40 citation statements)

References 47 publications

(48 reference statements)

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“…Utilization of renewable energy sources such as solar, wind, and biomass for power generation has become a viable solution to reduce greenhouse gas emission . India, being the bioenergy‐rich country with 25‐GW potential, has completed 500 biomass power and cogeneration projects by the end of 2017 .…”

Section: Introductionmentioning

confidence: 99%

Development of optimal reduced‐order model for gas turbine power plants using particle swarm optimization technique

Iqbal

Xavier²

2020

Int Trans Electr Energ Syst

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Summary Analysis of higher‐order gas turbine plant in real time would be tedious and expensive. In order to overcome this complexity, reduced‐order model for 5001M heavy‐duty gas turbine rated 18.2 MW has been obtained by Routh approximation, clustering technique, modified pole clustering, eigen permutation, Mihailov criterion, and Padé approximation algorithms. The step responses are obtained using MATLAB/Simulink and compared based on time domain specifications and performance index criteria. It indicates that the mixed method, namely, Routh approximation–Padé approximation algorithm–based reduced‐order model, retains the original characteristics. Further, particle swarm optimization (PSO) algorithm has also been applied to develop an optimal reduced‐order model. Based on the dynamic response against the load disturbance and set point variations, PSO‐based reduced‐order model has been identified as an optimal reduced‐order model for heavy‐duty gas turbine. The reduced‐order model proposed in this paper will be suitable for analyzing the dynamic behavior of heavy‐duty gas turbine plants in real‐time environment.

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Section: Introductionmentioning

confidence: 99%

Development of optimal reduced‐order model for gas turbine power plants using particle swarm optimization technique

Iqbal

Xavier²

2020

Int Trans Electr Energ Syst

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“…The output power of the wind farm fluctuate frequently, bringing great difference to grid-connection [1]. In order to facilitate the power grid dispatching, it is critical to forecast short-term wind power accurately.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model for Short-Term Wind Power Forecasting Based on MIV, Tversky Model and GA-BP Neural Network

et al. 2016

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Abstract. Wind power forecasting, which is necessary for wind farm, is significant to the dispatch of power grid since the characteristics of wind change intermittently. In this paper, a hybrid model for short-term wind power forecasting based on MIV, Tversky model and GA-BP neural network is formulated. The Mean Impact Value (MIV) method is used to monitor the input variable of BP neural network which will simplify the neural network model and reduce the training time. And the Tversky model is used for cluster analysis, which keeps watch over the similar training set of BP neural network. In addition, the Genetic Algorithm (GA) is used to optimize the initial weights and thresholds of BP neural network to achieve the global optimization. Simulation results show that the method combined with MIV, Tversky and GA-BP can improve the accuracy of short-term wind power forecasting.

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“…By the end of 2013, the world's wind-power capacity exceeded 290GW [1], [2]. However, the generated energy of wind farm is fluctuating, which brings great difficulties to grid-connection.…”

Section: Introductionmentioning

confidence: 99%

Research on Short-Term Wind Power Prediction Based on Combined Forecasting Models

et al. 2016

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Abstract. Short-Term wind power forecasting is crucial for power grid since the generated energy of wind farm fluctuates frequently. In this paper, a physical forecasting model based on NWP and a statistical forecasting model with optimized initial value in the method of BP neural network are presented. In order to make full use of the advantages of the models presented and overcome the limitation of the disadvantage, the equal weight model and the minimum variance model are established for wind power prediction. Simulation results show that the combination forecasting model is more precise than single forecasting model and the minimum variance combination model can dynamically adjust weight of each single method, restraining the forecasting error further.

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Global Renewable Energy-Based Electricity Generation and Smart Grid System for Energy Security

Cited by 79 publications

References 47 publications

Development of optimal reduced‐order model for gas turbine power plants using particle swarm optimization technique

Development of optimal reduced‐order model for gas turbine power plants using particle swarm optimization technique

A Hybrid Model for Short-Term Wind Power Forecasting Based on MIV, Tversky Model and GA-BP Neural Network

Research on Short-Term Wind Power Prediction Based on Combined Forecasting Models

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