Comparison of Artificial Intelligence-Based Solutions Applied to Economic Load Dispatch Problem

2020 ◽  
pp. 210-229
Author(s):  
Sarika Shrivastava ◽  
Piush Kumar
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Power Systems ◽  
Minimum Cost ◽  
Electric Power System ◽  
Optimization Techniques ◽  
Economic Load Dispatch ◽  
Practical Applications ◽  
Soft Computing Techniques ◽  
Loss Coefficients

The electric power system network is rapidly becoming more and more complex to meet energy requirements. With the development of integrated power systems, it becomes all the more necessary to operate the plant units most economically. More recently, soft computing techniques have received more attention and have been used in a number of successful and practical applications. In the chapter, artificial intelligence-based modern optimization techniques, the genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE), are used to solve the economic load dispatch related problems. In the chapter, the minimum cost is computed by adopting the genetic algorithm, PSO, and DE using the data from 15 generating units. Data has been taken from the published works containing loss coefficients are also given with the maximum-minimum power limits and cost function. All the techniques are implemented in MATLAB environment. Comparing the results obtained from GA, DE, and PSO-based method, better convergence was found in the PSO-based approach.

scholarly journals Computational Analysis of Different Artificial Intelligence Based Optimization Techniques for Optimal Power Flow and Economic Load Dispatch Problem

2013 ◽  
Vol 4 (1) ◽  
pp. 82-87
Author(s):  
Netra M Lokhande ◽  
Debirupa Hore
Keyword(s):  
Artificial Intelligence ◽  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Computational Analysis ◽  
Transmission Loss ◽  
Optimization Techniques ◽  
Bacterial Foraging Optimization ◽  
Economic Market ◽  
Optimal Power

The purpose of this paper is to present a computational Analysis of various Artificial Intelligence based optimization Techniques used to solve OPF problems. The various Artificial Intelligence methods such as Genetic Algorithm(GA), Particle Swarm Optimization(PSO), Bacterial Foraging Optimization(BFO), ANN are studied and analyzed in detail. The objective of an Optimal Power Flow (OPF) algorithm is to find steady state operation point which minimizes generation cost and transmission loss etc. or maximizes social welfare, load ability etc. while maintaining an acceptable system performance in terms of limits on generators’ real and reactive powers, power flow limits, output of various compensating devices etc. Traditionally, Classical optimization methods were used effectively to solve optimal power flow. But, recently due to the incorporation of FACTS devices and deregulation of power sector the traditional concepts and practices of power systems are superimposed by an economic market management and hence OPF have become more complex. So, in recent years, Artificial Intelligence (AI) methods have been emerged which can solve highly complex OPF problems at faster rate.

scholarly journals Genetic Algorithm and Particle Swarm Optimization Techniques for Inverted Pendulum Stabilization

2020 ◽  
Vol 9 (6) ◽  
pp. 657-660
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Evolutionary Computation ◽  
Inverted Pendulum ◽  
Biological Evolution ◽  
Particle Swarm ◽  
Continuous Variable ◽  
Optimization Techniques ◽  
Swarm Optimization ◽  
Soft Computing Techniques

Inverted Pendulum is a popular non-linear, unstable control problem where implementation of stabilizing the pole angle deviation, along with cart positioning is done by using novel control strategies. Soft computing techniques are applied for getting optimal results. The evolutionary computation forms the key research area for adaptation and optimization. The approach of finding optimal or near optimal solutions to the problem is based on natural evolution in evolutionary computation. The genetic algorithm is a method based on biological evolution and natural selection for solving both constrained and unconstrained problems. Particle swarm optimization is a stochastic search method inspired by collective behavior of animals like flocking of birds, schooling of fishes, swarming of bees etc. that is suited to continuous variable problems. These methods are applied to the inverted pendulum problem and their performance studied.

scholarly journals Optimization of engineering systems using genetic algorithm enhanced computational techniques

2021 ◽  
Author(s):  
Nicholas Farouk Ali
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Mechanical Engineering ◽  
Optimization Methods ◽  
System Optimization ◽  
Optimization Techniques ◽  
Engineering Applications ◽  
Engineering Problems ◽  
New Ideas

The field of optimization has been and continues to be an area of significant importance in the industry. From financial, industrial, social and any other sector conceivable, people are interested in improving the scheme of existing methodologies and products and/or in creating new ideas. Due to the growing need for humans to improve their lives and add efficiency to a system, optimization has been and still is an area of active research. Typically optimization methods seek to improve rather than create new ideas. However, the ability of optimization methods to mold new ideas should not be ruled out, since optimized solutions usually lead to new designs, which are in most cases unique. Combinatorial optimization is the term used to define the method of finding the best sequence or combination of variables or elements in a large complex system in order to attain a particular objective. This thesis promises to provide a panoramic view of optimization in general before zooming into a specific artificial intelligence technique in optimization. Detailed information on optimization techniques commonly used in mechanical engineering is first provided to ensure a clear understanding of the thesis. Moreover, the thesis highlights the differences and similarities, advantages and disadvantages of these techniques. After a brief study of the techniques entailed in optimization, an artificial intelligence algorithm, namely genetic algorithm, was selected, developed, improved and later applied to a wide variety of mechanical engineering problems. Ample examples from various fields of engineering are provided to illustrate the versatility of genetic algorithms. The major focus of this thesis is therefore the application of genetic algorithms to solve a broad range of engineering problems. The viability of the genetic algorithm (GA) as an optimization tool for mechanical engineering applications is assessed and discussed. Comparison between GA generated results and results found in the literature are presented when possible to underscore the power of GA to solve problems. Moreover, the disadvantages and advantages of the genetic algorithms are discussed based on the results obtained. The mechanical engineering applications studied include conceptual aircraft design, design of truss structures under various constraints and loading conditions, and armour design using established penetration analytical models. Results show that the genetic algorithm developed is capable of handling a wide range of problems, is an efficient cost effective tool, and often provides superior results when compared to other optimization methods found in the literature.

The Role of Artificial Intelligence and Fifth-Generation (5G) Technology in Mitigating Cyber Physical Attacks on Power Systems

2021 ◽  
pp. 424-441
Author(s):  
Kehinde Oluwafemi Olusuyi ◽  
Paul Kehinde Olulope ◽  
Abiodun Ernest Amoran ◽  
Eno Edet Peter
Keyword(s):  
Artificial Intelligence ◽  
Power Systems ◽  
Power System ◽  
Electric Power ◽  
New Technologies ◽  
Electric Power System ◽  
System A ◽  
Fifth Generation ◽  
Control Technologies ◽  
And Control

The present-day electric power system is an evolving cyber-physical system. Researchers and industry players in the energy world continue to deploy new technologies towards making the electric power system a smarter grid. This involves the integration of information, communication, and control technologies into the existing power grid in order to improve its stability, security, and operational efficiency. Reliance of the modern power system's applications such as state estimation, sequential control and data acquisition (SCADA) systems, phasor measurement units (PMUs), etc. on open communication technologies including the internet has exposed the smart grid to various vulnerabilities, threats, and cyber-physical attacks. This chapter seeks to exploit the robust synergy which exists between artificial intelligence (AI) and fifth-generation (5G) technology to mitigate these challenges. A comprehensive review of techniques which have hitherto proven efficient and/or effective in mitigating identified challenges was carried out with a view to availing researchers of future directions.

A Comparative Analysis on Economic Load Dispatch Problem Using Soft Computing Techniques

2020 ◽  
Vol 12 (2) ◽  
pp. 50-73 ◽  
Author(s):  
O.V. Singh ◽  
M. Singh
Keyword(s):  
Search Algorithm ◽  
Optimal Solution ◽  
Inequality Constraints ◽  
Optimization Techniques ◽  
Flower Pollination Algorithm ◽  
Economic Load Dispatch ◽  
Flower Pollination ◽  
Soft Computing Techniques ◽  
Equality And Inequality Constraints ◽  
Optimization Search

This article aims at solving economic load dispatch (ELD) problem using two algorithms. Here in this article, an implementation of Flower Pollination (FP) and the BAT Algorithm (BA) based optimization search algorithm method is applied. More than one objective is hoped to be achieve in this article. The combined economic emission dispatch (CEED) problem which considers environmental impacts as well as the cost is also solved using the two algorithms. Practical problems in economic dispatch (ED) include both nonsmooth cost functions having equality and inequality constraints which make it difficult to find the global optimal solution using any mathematical optimization. In this article, the ELD problem is expressed as a nonlinear constrained optimization problem which includes equality and inequality constraints. The attainability of the discussed methods is shown for four different systems with emission and without emission and the results achieved with FP and BAT algorithms are matched with other optimization techniques. The experimental results show that conferred Flower Pollination Algorithm (FPA) outlasts other techniques in finding better solutions proficiently in ELD problems.

The Application of Artificial Intelligence in Power System State Estimation with Intelligent Materials

2012 ◽  
Vol 485 ◽  
pp. 131-135 ◽  
Author(s):  
Yun Jing Liu ◽  
Feng Wen Wang
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Power Systems ◽  
Power System ◽  
State Estimation ◽  
Real Time ◽  
System State ◽  
Power System State Estimation ◽  
Artificial Intelligence Techniques

With the development of power systems, the problem of security, stability and economics has become increasingly important. Reliable real-time data base is the foundation of analysis of the systems security and stability. Power system state estimation is used to build reliable real-time model of the power network. It has the on-line security analysis function. Power systems are large, complex systems containing highly nonlinear components. Therefore, traditional approaches often have difficulties in finding the optimal solution efficiently. Artificial intelligence techniques are being applied to a wide range of practical problems in power system. With their ability to some laws of nature and mimic human reasoning, AI techniques such as fuzzy logic and genetic algorithm seem to be more efficient in dealing with large systems and complex problems. Artificial intelligence techniques have been applied in power system applications. This paper presents a method of adaptive genetic algorithm and fuzzy logic applied in phasor measurement placement and bad data identification. And simulation is evaluated on IEEE 22-bus power system.

scholarly journals Optimization of engineering systems using genetic algorithm enhanced computational techniques

2021 ◽  
Author(s):  
Nicholas Farouk Ali
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Mechanical Engineering ◽  
Optimization Methods ◽  
System Optimization ◽  
Optimization Techniques ◽  
Engineering Applications ◽  
Engineering Problems ◽  
New Ideas

The field of optimization has been and continues to be an area of significant importance in the industry. From financial, industrial, social and any other sector conceivable, people are interested in improving the scheme of existing methodologies and products and/or in creating new ideas. Due to the growing need for humans to improve their lives and add efficiency to a system, optimization has been and still is an area of active research. Typically optimization methods seek to improve rather than create new ideas. However, the ability of optimization methods to mold new ideas should not be ruled out, since optimized solutions usually lead to new designs, which are in most cases unique. Combinatorial optimization is the term used to define the method of finding the best sequence or combination of variables or elements in a large complex system in order to attain a particular objective. This thesis promises to provide a panoramic view of optimization in general before zooming into a specific artificial intelligence technique in optimization. Detailed information on optimization techniques commonly used in mechanical engineering is first provided to ensure a clear understanding of the thesis. Moreover, the thesis highlights the differences and similarities, advantages and disadvantages of these techniques. After a brief study of the techniques entailed in optimization, an artificial intelligence algorithm, namely genetic algorithm, was selected, developed, improved and later applied to a wide variety of mechanical engineering problems. Ample examples from various fields of engineering are provided to illustrate the versatility of genetic algorithms. The major focus of this thesis is therefore the application of genetic algorithms to solve a broad range of engineering problems. The viability of the genetic algorithm (GA) as an optimization tool for mechanical engineering applications is assessed and discussed. Comparison between GA generated results and results found in the literature are presented when possible to underscore the power of GA to solve problems. Moreover, the disadvantages and advantages of the genetic algorithms are discussed based on the results obtained. The mechanical engineering applications studied include conceptual aircraft design, design of truss structures under various constraints and loading conditions, and armour design using established penetration analytical models. Results show that the genetic algorithm developed is capable of handling a wide range of problems, is an efficient cost effective tool, and often provides superior results when compared to other optimization methods found in the literature.

Sign in / Sign up

Export Citation Format

Share Document