scholarly journals A Multi-Objective Optimal Power Flow Control of Electrical Transmission Networks Using Intelligent Meta-Heuristic Optimization Techniques

2021 ◽  
Vol 13 (9) ◽  
pp. 4979
Author(s):  
Hatem Diab ◽  
Mahmoud Abdelsalam ◽  
Alaa Abdelbary
Keyword(s):  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Operating Conditions ◽  
Control Technique ◽  
Heuristic Optimization ◽  
Multi Objective ◽  
Optimal Power

Optimal power flow (OPF) is considered one of the most critical challenges that can substantially impact the sustainable performance of power systems. Solving the OPF problem reduces three essential items: operation costs, transmission losses, and voltage drops. An intelligent controller is needed to adjust the power system’s control parameters to solve this problem optimally. However, many constraints must be considered that make the design process of the OPF algorithm exceedingly tricky due to the increased number of limitations and control variables. This paper proposes a multi-objective intelligent control technique based on three different meta-heuristic optimization algorithms: multi-verse optimization (MVO), grasshopper optimization (GOA), and Harris hawks optimization (HHO) to solve the OPF problem. The proposed control techniques were validated by applying them to the IEEE-30 bus system under different operating conditions through MATLAB simulations. The proposed techniques were then compared with the particle swarm optimization (PSO) algorithm, which is very popular in the literature studying how to solving the OPF problem. The obtained results show that the proposed methods are more effective in solving the OPF problem when compared to the commonly used PSO algorithm. The proposed HHO, in particular, shows that it can form a reliable candidate in solving power systems’ optimization problems.

scholarly journals OPTIMAL POWER FLOW WITH TCSC DEVICE USING CUCKOO OPTIMIZATION ALGORITHM

2018 ◽  
Vol 54 (3A) ◽  
pp. 52
Author(s):  
Duong Thanh Long
Keyword(s):  
Power System ◽  
Optimization Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Power Transmission ◽  
Optimization Techniques ◽  
Operating Conditions ◽  
Cuckoo Optimization Algorithm ◽  
Facts Devices ◽  
Optimal Power

Optimal Power Flow (OPF) problem is an optimization tool through which secure and economic operating conditions of power system is obtained. In recent years, Flexible AC Transmission System (FACTS) devices, have led to the development of controllers that provide controllability and flexibility for power transmission. Series FACTS devices such as Thyristor controlled series compensators (TCSC), with its ability to directly control the power flow can be very effective to power system security. Thus, integration TCSC in the OPF is one of important current problems and is a suitable method for better utilization of the existing system. This paper is applied Cuckoo Optimization Algorithm (COA) for the solution of the OPF problem of power system equipped with TCSC. The proposed approach has been examined and tested on the IEEE 30-bus system. The results presented in this paper demonstrate the potential of COA algorithm and show its effectiveness for solving the OPF problem with TCSC devices over the other evolutionary optimization techniques.

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 Contingency-Constrained Optimal Power Flow Using Simplex-Based Chaotic-PSO Algorithm

2011 ◽  
Vol 2011 ◽  
pp. 1-13 ◽  
Author(s):  
Zwe-Lee Gaing ◽  
Chia-Hung Lin
Keyword(s):  
Power Systems ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Transmission Loss ◽  
Chaotic Map ◽  
Pso Algorithm ◽  
Voltage Profile ◽  
Solution Quality ◽  
Optimal Power ◽  
Loading Effects

This paper proposes solving contingency-constrained optimal power flow (CC-OPF) by a simplex-based chaotic particle swarm optimization (SCPSO). The associated objective of CC-OPF with the considered valve-point loading effects of generators is to minimize the total generation cost, to reduce transmission loss, and to improve the bus-voltage profile under normal or postcontingent states. The proposed SCPSO method, which involves the chaotic map and the downhill simplex search, can avoid the premature convergence of PSO and escape local minima. The effectiveness of the proposed method is demonstrated in two power systems with contingency constraints and compared with other stochastic techniques in terms of solution quality and convergence rate. The experimental results show that the SCPSO-based CC-OPF method has suitable mutation schemes, thus showing robustness and effectiveness in solving contingency-constrained OPF problems.

scholarly journals Review on population-based metaheuristic search techniques for optimal power flow

2019 ◽  
Vol 15 (1) ◽  
pp. 373
Author(s):  
Muhammad Affiq Abd Rahman ◽  
Bazilah Ismail ◽  
Kanendra Naidu ◽  
Mohd Khairil Rahmat
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
System Operation ◽  
Linear Solution ◽  
Search Techniques ◽  
Optimal Power ◽  
Metaheuristic Search ◽  
Non Linear

<span>Optimal power flow (OPF) is a non-linear solution which is significantly important in order to analyze the power system operation. The use of optimization algorithm is essential in order to solve OPF problems. <br /> The emergence of machine learning presents further techniques which capable to solve the non-linear problem. The performance and the key aspects which enhances the effectiveness of these optimization techniques are compared within several metaheuristic search techniques. This includes the operation of particle swarm optimization (PSO) algorithm, firefly algorithm (FA), artificial bee colony (ABC) algorithm, ant colony optimization (ACO) algorithm and differential evolution (DE) algorithm. This paper reviews on the key elements that need to be considered when selecting metaheuristic techniques to solve OPF problem in power <br /> system operation.</span>

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