An ant colony optimization algorithm-based emergency control strategy for voltage collapse mitigation

2012 ◽  
Vol 3 (2) ◽  
pp. 147-156 ◽  
Author(s):  
R. A. El-Sehiemy ◽  
A. A. A. El Ela ◽  
A. M. M. Kinawy ◽  
M. T. Mouwafia
Keyword(s):  
Power Systems ◽  
Ant Colony Optimization ◽  
Power Flow ◽  
Reactive Power ◽  
Test System ◽  
Ant Colony ◽  
Voltage Collapse ◽  
Ant Colony Optimization Algorithm ◽  
Preventive Control ◽  
Control Actions

Abstract This paper presents optimal preventive control actions using ant colony optimization (ACO) algorithm to mitigate the occurrence of voltage collapse in stressed power systems. The proposed objective functions are: minimizing the transmission line losses as optimal reactive power dispatch (ORPD) problem, maximizing the preventive control actions by minimizing the voltage deviation of load buses with respect to the specified bus voltages and minimizing the reactive power generation at generation buses based on control variables under voltage collapse, control and dependent variable constraints using proposed sensitivity parameters of reactive power that dependent on a modification of Fast Decoupled Power Flow (FDPF) model. The proposed preventive actions are checked for different emergency conditions while all system constraints are kept within their permissible limits. The ACO algorithm has been applied to IEEE standard 30-bus test system. The results show the capability of the proposed ACO algorithm for preparing the maximal preventive control actions to remove different emergency effects.

A Multi-Objective Fuzzy-Based Procedure for Reactive Power-Based Preventive Emergency Strategy

2014 ◽  
Vol 13 ◽  
pp. 91-102 ◽  
Author(s):  
Ragab A. El Sehiemy ◽  
Adel A. Abou El Ela ◽  
Abdelallah Shaheen
Keyword(s):  
Power Systems ◽  
Reactive Power ◽  
Test System ◽  
Operating Conditions ◽  
Fuzzy Linear Programming ◽  
Objective Functions ◽  
Transmission Losses ◽  
The West ◽  
Multi Objective ◽  
Control Actions

This paper proposes a multi-objective fuzzy linear programming (MFLP) procedure for maximizing the effects of preventive reactive power control actions to overcome any emergency condition when they occurred. The proposed procedure is very significant and seeks to eliminate violation constraints and give an optimal reactive power reserve for multi-operating conditions. The proposed multi-objective functions are: minimizing the real transmission losses, maximizing the reactive power reserve at certain generator and maximizing the reactive power reserve at all generation systems and/or switchable devices. The proposed MFLP is applied to 5-bus test system and the West Delta region system as a part of the Egyptian Unified network. The numerical results show that the proposed MFLP technique achieves a minimum real power loss with maximal reactive reserve for power systems for different operating conditions.

Decline of Real Power Loss by the Combination of Ant Colony Optimization and Simulated Annealing Algorithm

2016 ◽  
Vol 23 ◽  
pp. 113-119 ◽  
Author(s):  
K. Lenin ◽  
B. Ravindhranath Reddy ◽  
M. Suryakalavathi
Keyword(s):  
Simulated Annealing ◽  
Ant Colony Optimization ◽  
Power Loss ◽  
Simulated Annealing Algorithm ◽  
Reactive Power ◽  
Test System ◽  
Ant Colony ◽  
Local Optimum ◽  
Real Power ◽  
Combined Algorithm

Combination of ant colony optimization (ACO) algorithm and simulated annealing (SA) algorithm has been done to solve the reactive power problem.In this proposed combined algorithm (CA), the leads of parallel, collaborative and positive feedback of the ACO algorithm has been used to apply the global exploration in the current temperature. An adaptive modification threshold approach is used to progress the space exploration and balance the local exploitation. When the calculation process of the ACO algorithm falls into the inactivity, immediately SA algorithm is used to get a local optimal solution. Obtained finest solution of the ACO algorithm is considered as primary solution for SA algorithm, and then a fine exploration is executed in the neighborhood. Very importantly the probabilistic jumping property of the SA algorithm is used effectively to avoid solution falling into local optimum. The proposed combined algorithm (CA) approach has been tested in standard IEEE 30 bus test system and simulation results show obviously about the better performance of the proposed algorithm in reducing the real power loss with control variables within the limits.

Power Flow Calculation for Distribution System Including PV Type Distributed Generation

2014 ◽  
Vol 694 ◽  
pp. 159-162
Author(s):  
Fei Han ◽  
Ning Zhou ◽  
Jian Wei Ma ◽  
Xian Ling Yu
Keyword(s):  
Ant Colony Optimization ◽  
Distributed Generation ◽  
Distribution System ◽  
Power Flow ◽  
Reactive Power ◽  
Simulation Software ◽  
Ant Colony ◽  
Power Correction ◽  
Calculation Algorithm ◽  
Flow Solution

After distribution network with PV type distributed generation, it emerged PV nodes. Advanced forward and backward substitution method is proposed based on ant colony optimization method to improve power flow solution, which is a method based on ant colony optimization calculation algorithm of reactive power correction, improved the model of PV type distributed generation in the power flow calculation.Use PSSSINCAL power system simulation software to set up the model of distribution System including PV type distributed generation. Through the results of simulation calculation show that the algorithm can cope with power flow solution for distribution system including PV type distributed generation effectively, and the convergence property is very good.

Binary Adaptive Ant Colony Optimization in Reactive Power Optimization

2012 ◽  
Vol 616-618 ◽  
pp. 2091-2096 ◽  
Author(s):  
Hong Hong ◽  
Fang Liu
Keyword(s):  
Ant Colony Optimization ◽  
State Transition ◽  
Reactive Power ◽  
Ant Colony ◽  
Reactive Power Optimization ◽  
Ant Colony Optimization Algorithm ◽  
Information Update ◽  
Transition Rules ◽  
Update Rules ◽  
State Transition Rules

This article proposed an Adaptive Binary Ant Colony Optimization Algorithm, which is based on the dual network diagram, designed to state transition rules and information update rules, and then according to the algorithm processes adjust information volatilizing factor dynamically, Verify the validity and superiority of the algorithm.

scholarly journals ATC using FACTS Devices in Deregulated Power Systems

2017 ◽  
Vol 7 (2 (S)) ◽  
pp. 214
Author(s):  
Prakash Burade ◽  
Rajendra Sadafale ◽  
Anand Satpute
Keyword(s):  
Genetic Algorithm ◽  
Power Generation ◽  
Power Systems ◽  
Ant Colony Optimization ◽  
Power Flow ◽  
Reactive Power ◽  
Integer Optimization ◽  
Thermal Limits ◽  
Facts Devices ◽  
Deregulated Power Systems

A Repeated Power Flow with FACTS devices including ATC is used to evaluate the feasible ATC value within real and reactive power generation limits, line thermal limits, voltage limits and FACTS operation limits. An IEEE-30 bus system is used to demonstrate the effectiveness of the algorithm as an optimization tool to enhance ATC. A Genetic Algorithm technique is used for validation purposes. Introduction of FACTS devices in a right combination of location and parameters could enhance ATC and Ant Colony optimization can be efficiently used for this kind of nonlinear integer optimization.

Ant Colony Optimization Algorithm for Electrical Power Systems Applications

2022 ◽  
pp. 37-59
Author(s):  
Ragab A. El-Sehiemy ◽  
Almoataz Y. Abdelaziz
Keyword(s):  
Power Systems ◽  
Ant Colony Optimization ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Electrical Power ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Electrical Power System ◽  
Electrical Power Systems ◽  
Recent Developments

Optimization has been an active area of research for several decades. As many real-world optimization problems become increasingly complex, better optimization algorithms are always needed. Recently, meta-heuristic global optimization algorithms have become a popular choice for solving complex and intricate problems, which are otherwise difficult to solve by traditional methods. This chapter reviews the recent applications of ant colony optimization (ACO) algorithm in the field of electrical power systems. Also, the progress of the ACO algorithm and its recent developments are discussed. This chapter covers the aspects like (1) basics of ACO algorithm, (2) progress of ACO algorithm, (3) classification of electrical power system applications, and (4) future of ACO for modern power systems application.

Optimal Location of TCSC, TCPS, and SVC Devices for Solving OPF and ORPD Problem Using Different Evolutionary Optimization Techniques

2018 ◽  
pp. 244-273
Keyword(s):  
Power Systems ◽  
Power System ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Phase Shifter ◽  
Reactive Power ◽  
Test System ◽  
Optimal Location ◽  
Thyristor Control ◽  
Evolutionary Optimization Techniques

The secure operation of power system has become a topmost issue in today's largely complicated interconnected power systems. This chapter presents the implementation of grey wolf optimization (GWO), teaching-learning-based optimization (TLBO), biogeography-based optimization (BBO), krill herd algorithm (KHA), chemical reaction optimization (CRO), and hybrid CRO (HCRO) approaches to find the optimal location of various FACTS devices such as thyristor control series compensator (TCSC), thyristor control phase shifter (TCPS), and static VAR compensator (SVC) to solve optimal power flow (OPF) and optimal reactive power dispatch (ORPD) in power system. In this chapter, a standard IEEE 30-bus test system with multiple TCSC and TCPS and SVC devices are used for different single and multi-objective functions to validate the performance of the proposed methods. The simulation results validate the ability of the HCRO to produce better optimal solutions compared to GWO, TLBO, BBO, KHA, and CRO algorithms.

Optimal reactive power dispatch using ant colony optimization algorithm

2011 ◽  
Vol 93 (2) ◽  
pp. 103-116 ◽  
Author(s):  
A. A. Abou El-Ela ◽  
A. M. Kinawy ◽  
R. A. El-Sehiemy ◽  
M. T. Mouwafi
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Reactive Power ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Optimal Reactive Power Dispatch ◽  
Power Dispatch ◽  
Reactive Power Dispatch

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

2020 ◽  
Vol 10 (1) ◽  
pp. 738
Author(s):  
Vijo M Joy ◽  
S. Krishnakumar
Keyword(s):  
Power Generation ◽  
Power Systems ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Peak Load ◽  
Back Propagation ◽  
Minimum Cost ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Load Scheduling

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.

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