Development of Hybrid Algorithm Based on PSO and NN to Solve Economic Emission Dispatch Problem

Abstract

The electric power generation system has always the significant location in the power system, and it should have an efficient and economic operation. This consists of the generating unit's allocation with minimum fuel cost and also considers the emission cost. In this paper we have intended to propose a hybrid technique to optimize the economic and emission dispatch problem in power system. The hybrid technique is used to minimize the cost function of generating units and emission cost by balancing the total load demand and to decrease the power loss. This proposed technique employs Particle Swarm Optimization (PSO) and Neural Network (NN). PSO is one of the computational techniques that use a searching process to obtain an optimal solution and neural network is used to predict the load demand. Prior to performing this, the neural network training method is used to train all the generating power with respect to the load demand. The economic and emission dispatch problem will be solved by the optimized generating power and predicted load demand. The proposed hybrid intelligent technique is implemented in MATLAB platform and its performance is evaluated.

Neural Network dan Particle Swam Optimization untuk Penunjang Keputusan Antipasi Mahasiswa Pra Lulus Bekerja Sesuai Bidang

“…Dari permasalahan dan uraian penelitian sebelumnya diatas maka pada penelitian ini dilakukan metode kombinasi antara Neural Network dengan menggunakan fitur selektif berupa PSO. Sehingga dari penelitian ini diperoleh output yang dapat digunakan sebagai penunjang keputusan untuk mengetahui model akurasi lulusan yang sesuai bidang disuatu perguruan tinggi secara akurat sebagai antipasi mahasiwa pra lulusan yang bekerja sesuai bidang [15,16] Metode 3-fold cross validation menjadi metode standar dalam pembelajaran (training) dan pengujian data. Penggunaan data set bersifat publik menjadikan penelitian yang dapat di teliti ulang, tidak dapat dipungkiri, dan dapat diverifikasi [18].…”

Developing Chaotic Artificial Ecosystem-Based Optimization Algorithm for Combined Economic Emission Dispatch

“…Recently, researchers had made modifying and developing standalone ways by combining the effective features of two or more methods to become a hybrid method and thereby to attain superior performance than standalone ways. A number of the most newly introduced hybrid methods to achieve the optimum solution for the CEED problem are ACO-ABC-HS algorithm [16], PSOGSA [17], RGA and DE [18], backtracking search algorithm with sequential quadratic programming [19], MHBA [20], CSA and DE [21], FFA-BA [22], PSO-NN [23], DE-SA [24], and gradient search method and improved Jaya algorithm [25]. But the long computational time is sometimes one of the hybrid algorithm drawbacks wherever every one of the algorithms performs separately into the problem and adds more complexities [5].…”

The Optimization of Neural Network Based PSO Feature Selection in the Classification of Graduates Working According to Their Field

“…The combination method between the Neural Network and the PSO selection feature is expected that the output of the resulting model has high accuracy so that it can produce. The purpose of this study is to find a correlation between the influential attributes then increase the accuracy of the neural network algorithm at once finding the optimal model to solve problems for graduates so that they work according to their fields accurate decisions in making anticipatory decision decisions for the pre-graduate student so that they are suitable to work in the field [5] [6].…”