Solution of Economic Load Dispatch problem in Power System using Lambda Iteration and Back Propagation Neural Network Methods

Machavarapu, Suman; Rao, M. Venu Gopala; Hanumaiah, A; Rajesh, K.

doi:10.15676/ijeei.2016.8.2.8

Cited by 18 publications

(8 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The architecture of MNN is as given in Figure 3 [14,15]. It has an input, hidden and output layers with n,p and m number of neurons.…”

Section: Back Propagation Algorithmmentioning

confidence: 99%

Machine Learning Algorithm Based Static VAR Compensator to Enhance Voltage Stability of Multi-machine Power System

Machavarapu¹,

Rao²,

Rao³

2019

MMEP

View full text Add to dashboard Cite

A novel Extreme learning Machine (ELM) algorithm based tuning of the parameters of the SVC FACTS controller was implemented in order to control voltage at various buses over a wide range. The ELM algorithm is a non-iterative method which forecasts the parameters of SVC FACTS controller quickly and effectively while Back Propagation Neural Network (BPNN) algorithm is an iterative method which takes a long time for training as well prediction of parameters. The load perturbation is one of the nonlinear disturbances which is considered to investigate the operational capability of the control methodology. Standard IEEE 5 and 30 bus systems are considered as test systems and operation of two models of SVC observed with the BPNN and ELM controllers. The weakest bus is identified using L-Index method which is the optimal location of the SVC FACTS. Results show that the novel ELM method expeditiously and efficiently tunes the parameters of the SVC FACTS controller online such that the voltage regulated to desired value when there is a perturbation in load.

show abstract

“…The architecture of MNN is as given in Figure 3 [14,15]. It has an input, hidden and output layers with n,p and m number of neurons.…”

Section: Back Propagation Algorithmmentioning

confidence: 99%

Machine Learning Algorithm Based Static VAR Compensator to Enhance Voltage Stability of Multi-machine Power System

Machavarapu¹,

Rao²,

Rao³

2019

MMEP

View full text Add to dashboard Cite

show abstract

“…At present, this is the superlative neural network learning algorithm and the majority of neural network presentations are grounded on this algorithm [8]. In a BP network, the transfer function for neurons is frequently a differentiable function with the sigmoid.…”

Section: B Backpropagation Neural Networkmentioning

confidence: 99%

A Modified Ant Colony Optimization Algorithm for Power Scheduling

Joy*¹,

Krishnakumar²

2019

IJITEE

View full text Add to dashboard Cite

The purpose of this work is to solve the problems related to the power system in an efficient manner by assigning the optimal values. To meet the demand loads with good quality and quantity is a challenging problem in the field of the power system. Artificial Neural Network is realized in the field of energy management and load scheduling. The Backpropagation algorithm is used for the training purpose. It has the aspects of the quick meeting on the local bests but it gets stuck in local minima. To overcome this drawback an Ant Colony Optimization algorithm is presented to allocate optimal output values for the power system. This has the capacity for searching the global optimal solution. Present work modifies the ant colony optimization algorithm with backpropagation. This hybrid algorithm accelerates the network and improves its accuracy. The ant colony optimization algorithms provide an accurate optimal combination of weights and then use backpropagation technique to obtain the accurate optimal solution rapidly. The result shows that the present system is more efficient and effective. These algorithms significantly reduce the peak load and minimize the energy consumption cost

show abstract

“…They reduced the manual effort by separating the least possible loads from the neural network. A Hydro-thermal scheduling based on ANN was proposed by Suman, et al [9]. In this scheduling method, the total load is shared between hydro and thermal power stations according to the cost of power generation.…”

Section: Introductionmentioning

confidence: 99%

Optimal design of adaptive power scheduling using modified ant colony optimization algorithm

Joy

Krishnakumar

2020

IJECE

View full text Add to dashboard Cite

For generating and distributing an economic load scheduling approach, artificial neural network (ANN) has been introduced, because power generation and power consumption are economically non-identical. An efficient load scheduling method is suggested in this paper. Normally the power generation system fails due to its instability at peak load time. Traditionally, load shedding process is used in which low priority loads are disconnected from sources. The proposed method handles this problem by scheduling the load based on the power requirements. In many countries the power systems are facing limitations of energy. An efficient optimization algorithm is used to periodically schedule the load demand and the generation. Ant colony optimization (ACO) based ANN is used for this optimal load scheduling process. The present work analyse the technical economical and time-dependent limitations. Also the works meets the demanded load with minimum cost of energy. Inorder to train ANN back propagation (BP) technics is used. A hybrid training process is described in this work. Global optimization algorithms are used to provide back propagation with good initial connection weights.

show abstract

Solution of Economic Load Dispatch problem in Power System using Lambda Iteration and Back Propagation Neural Network Methods

Cited by 18 publications

References 12 publications

Machine Learning Algorithm Based Static VAR Compensator to Enhance Voltage Stability of Multi-machine Power System

Machine Learning Algorithm Based Static VAR Compensator to Enhance Voltage Stability of Multi-machine Power System

A Modified Ant Colony Optimization Algorithm for Power Scheduling

Optimal design of adaptive power scheduling using modified ant colony optimization algorithm

Contact Info

Product

Resources

About