Designing a GA-Based Robust Controller For Load Frequency Control (LFC)

Microgrids have become more and more popular their usefulness as a renewable energy resource has been recognized. The core ability and promise of microgrids is addressing the environmental concerns due to climate change that have been growing during recent years. The innovation of microgrids is that they are designed to operate either in island mode or interconnected with the main grid system. However, when the microgrid operates in islanded mode, faults may occur which can cause system collapse or even blackout. Load curtailment schemes can be utilized to decrease the quantity of associated load to a level that can be securely supported by accessible generation in isolated mode. The main goal of this research is to evaluate the optimal amount of shedding power considering sustainable power sources, with the help of primary and secondary adjustments of the generator to restore the frequency to the allowed range. Particle Swarm Optimization algorithm is applied in this paper to determine the distributed shedding power on each demand load bus which can improve the voltage quality of the isolated microgrid system. The effectiveness of the proposed method is demonstrated through the simulation of IEEE 16- bus microgrid.

“…It's a reaction of a power source that has a speeder or an AFC. These frequency controllers will increase the power of generators in relation to the frequency [20,21].…”

Section: A Primary and Secondary Frequency Adjustmentmentioning

confidence: 99%

Section: A Primary and Secondary Frequency Adjustmentmentioning

confidence: 99%

Load Shedding in Microgrids with Consideration of Voltage Quality Improvement

Phung

2021

“…In [12], for instance, the authors proposed a method in which the determination of the load shedding localization relies heavily on the two buses' phase angle sensitivity. More recently, however, several researchers have developed distinct load shedding methods, enabling to adjust the frequency and voltage within the required or demanded limits while reducing the amount of load shedding [13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Optimal Shedding Against Voltage Collapse Based on Genetic Algorithm

Zdiri

Alshammari

Alzamil

et al. 2021

The prevalent tendency in power transmission systems is to operate closer and closer to the energy limit, rendering system voltage instability a commonly widespread phenomenon. It is, therefore, necessary that certain remedial corrective controls need be undertaken whenever these systems tend towards failure. In this respect, load shedding stands as a major correction mechanism and such a failure can be prevented and nominal system voltage can be resumed. It is worth noting however that load shedding must be implemented very carefully to ensure the satisfaction of both the customer and the electricity-production company. In this context, our focus of interest is laid on load and machine shedding against voltage collapse as an effective corrective method. It is important to note that such a problem turns out to be commonly defined as an optimization problem under constraints. Using genetic algorithms as resolution methods, the application of the proposed methods was implemented on the 14-node IEEE test network, while considering a number of different case studies.

“…In singleobjective optimization models and multi-objective problems, the endeavor is to find a set of optimal solutions for conflicting objectives which are not necessarily superior corresponding to all objectives, called the non-dominated Pareto optimal solutions [19][20][21]. Utilizing evolutionary algorithms such as Non-dominated Sorting Genetic Algorithm (NSGA) [22][23][24][25], Multi-Colony ACO (MOACO) [26], and Multi-Objective Particle Swarm Optimization (MOPSO) [27,28], establishes a set of solutions which are subject to decision-makers' selection from the optimum. Although single-objective maintenance scheduling problems have been broadly addressed in the literature, the number of studies on multi-objective maintenance problems is quite limited.…”

Section: Introductionmentioning

confidence: 99%

Integrated Preventive Maintenance Scheduling Model with Redundancy for Cutting Tools on a Single Machine

Tavassoli¹,

Sakhavand²,

Fazeli³

2020

In this paper, we present an integrated multi-objective framework of a single machine for a single cutting tool problem. Our maintenance policy is based on performing minimal repairs in case of a minor failure and Preventive Maintenance (PM) to avoid a major failure that results in the replacement of the tool. This framework allows simultaneous optimization of the two conflicting time and cost objectives. A redundant system is proposed as a part of the model to assist the production line under a major failure. In addition, the tool’s preventive maintenance time is synchronized with the completion of the machine tool’s work cycle to reduce the machine’s set-up time. The model was optimized using a customized Non-dominated Sorting Genetic Algorithm (NSGA-II). An experimental study based on real-market data was conducted and the results were compared with the ones obtained from classical methods.