Load Shedding Scheme of Offshore-Island Power Grids of Renewable Energy Using Neural Network

2020 ◽  
Author(s):  
Shih-Chieh Hsieh
Keyword(s):  
Neural Network ◽  
Renewable Energy ◽  
Power Grids ◽  
Load Shedding ◽  
Offshore Island

Frequency and inertia response effects, capabilities, and possibilities in renewable energy sources

2021 ◽  
Vol 14 ◽  
Author(s):  
Jishu Mary Gomez ◽  
Prabhakar Karthikeyan Shanmugam
Keyword(s):  
Renewable Energy ◽  
Power System ◽  
Frequency Control ◽  
Renewable Energy Sources ◽  
Energy Systems ◽  
Load Shedding ◽  
System Stability ◽  
Frequency Regulation ◽  
Control Schemes ◽  
Inertia Response

Background & Objectives: The global power system is in a state of continuous evolution, incorporating more and more renewable energy systems. The converter-based systems are void of inherent inertia control behavior and are unable to curb minor frequency deviations. The traditional power system, on the other hand, is made up majorly of synchronous generators that have their inertia and governor response for frequency control. For improved inertial and primary frequency response, the existing frequency control methods need to be modified and an additional power reserve is to be maintained mandatorily for this purpose. Energy self-sufficient renewable distributed generator systems can be made possible through optimum active power control techniques. Also, when major global blackouts were analyzed for causes, solutions, and precautions, load shedding techniques were found to be a useful tool to prevent frequency collapse due to power imbalances. The pre-existing load shedding techniques were designed for traditional power systems and were tuned to eliminate low inertia generators as the first step to system stability restoration. To incorporate emerging energy possibilities, the changes in the mixed power system must be addressed and new frequency control capabilities of these systems must be researched. Discussion: In this paper, the power reserve control schemes that enable frequency regulation in the widely incorporated solar photovoltaic and wind turbine generating systems are discussed. Techniques for Under Frequency Load Shedding (UFLS) that can be effectively implemented in renewable energy enabled micro-grid environment for frequency regulation are also briefly discussed. The paper intends to study frequency control schemes and technologies that promote the development of self- sustaining micro-grids. Conclusion: The area of renewable energy research is fast emerging with immense scope for future developments. The comprehensive literature study confirms the possibilities of frequency and inertia response enhancement through optimum energy conservation and control of distributed energy systems.

Power Quality Assessment and Enhancement in a Grid Connected Renewable Energy System Using Dynamic Voltage Restorer

2014 ◽  
Vol 573 ◽  
pp. 716-721
Author(s):  
S. Rajeshbabu ◽  
B.V. Manikandan
Keyword(s):  
Neural Network ◽  
Renewable Energy ◽  
Power Quality ◽  
Energy System ◽  
Dynamic Voltage Restorer ◽  
Renewable Energy System ◽  
Voltage Restorer ◽  
Point Of Common Coupling ◽  
Dynamic Voltage ◽  
Common Coupling

Renewable energy sources provide the additional/satisfy the power to the consumer through power electronics interfaces and integrated with the grid. In grid integration power quality is one of the important parameter that need to be paying more attention. This proposed work focuses on power quality issues in a grid connected renewable energy system. Power quality issues will arises due to many factors here with the by introducing a fault condition in a grid connected renewable energy system the measurements were made at the point of common coupling and the mitigation is done with the help of a dynamic voltage restorer. The dynamic voltage restorer is a device which offers series compensation activated by neural network based controller. The sag improvement and the total harmonic assessment were made at the point of common coupling. Keywords: Neural network, Point of common coupling, Renewable energy source, Power quality, Dynamic voltage restorer ,electric grid.

scholarly journals Solar Irradiance Prediction using Neural Model

2018 ◽  
Vol 8 (3) ◽  
Author(s):  
Raj Kumar Yadav ◽  
Nivedita Sethy
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Renewable Energy ◽  
Mean Square Error ◽  
Solar Irradiance ◽  
Neural Model ◽  
Accurate Prediction ◽  
Solar Irradiation ◽  
National Renewable Energy Laboratory ◽  
Mean Square

The accurate prediction of solar irradiation has been a leading problem for better energy scheduling approach. Hence in this paper, an Artificial neural network based solar irradiance is proposed for five days duration the data is obtained from National Renewable Energy Laboratory, USA and the simulation were performed using MATLAB 2013. It was found that the neural model was able to predict the solar irradiance with a mean square error of 0.0355.

scholarly journals Optimization of Stochastic Lossy Transport Networks and Applications to Power Grids

2021 ◽  
Author(s):  
Alessandro Zocca ◽  
Bert Zwart
Keyword(s):  
Mathematical Model ◽  
Renewable Energy ◽  
Smart Grids ◽  
Transport Network ◽  
Power Grids ◽  
Performance Gain ◽  
Power Losses ◽  
Transport Networks ◽  
Trade Off ◽  
Stochastic Load

Motivated by developments in renewable energy and smart grids, we formulate a stylized mathematical model of a transport network with stochastic load fluctuations. Using an affine control rule, we explore the trade-off between the number of controllable resources in a lossy transport network and the performance gain they yield in terms of expected power losses. Our results are explicit and reveal the interaction between the level of flexibility, the intrinsic load uncertainty, and the network structure.

scholarly journals Challenges in Grid Integration of Renewable Energy Systems via Inverter: A Survey

2021 ◽  
Vol 7 (3) ◽  
pp. 30-33
Author(s):  
Sourabh Kedar ◽  
Mr. Santosh Singh Negi
Keyword(s):  
Renewable Energy ◽  
Reactive Power ◽  
Power Grids ◽  
Grid Integration ◽  
Solar Photovoltaic ◽  
Renewable Energy Systems ◽  
Pv Systems ◽  
The Past ◽  
Network Coordination ◽  
The Individual

Solar photovoltaic (PV) systems have mainly been used in the past decade. Inverter-powered photovoltaic grid topologies are widely used to meet electricity demand and to integrate forms of renewable energy into power grids. Meeting the growing demand for electricity is a major challenge today. This paper provides a detailed overview of the topological trend of inverters with connection to the photovoltaic grid, as well as the advantages, disadvantages and main characteristics of the individual inverters. For proper integration into a network, coordination between the supporting devices used for reactive power compensation and their optimal reactive power capacity for grid current stability is important.

scholarly journals ELMAN-RECURRENT NEURAL NETWORK FOR LOAD SHEDDING OPTIMIZATION

SINERGI ◽  
2020 ◽  
Vol 24 (1) ◽  
pp. 29
Author(s):  
Widi Aribowo
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Power System ◽  
Recurrent Neural Networks ◽  
Load Shedding ◽  
Feed Forward Neural Network ◽  
Linear Problems ◽  
Elman Recurrent Neural Network ◽  
And Control

Load shedding plays a key part in the avoidance of the power system outage. The frequency and voltage fluidity leads to the spread of a power system into sub-systems and leads to the outage as well as the severe breakdown of the system utility.  In recent years, Neural networks have been very victorious in several signal processing and control applications.  Recurrent Neural networks are capable of handling complex and non-linear problems. This paper provides an algorithm for load shedding using ELMAN Recurrent Neural Networks (RNN). Elman has proposed a partially RNN, where the feedforward connections are modifiable and the recurrent connections are fixed. The research is implemented in MATLAB and the performance is tested with a 6 bus system. The results are compared with the Genetic Algorithm (GA), Combining Genetic Algorithm with Feed Forward Neural Network (hybrid) and RNN. The proposed method is capable of assigning load releases needed and more efficient than other methods. 

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