M. A. Abido

Optimal placement of relay nodes in wireless sensor network using artificial bee colony algorithm

Deploying sensor nodes randomly most of the time generates initial communication hole even in highly dense networks. These communication holes cannot be totally eliminated even when the deployment is done in a structured manner. In either case, the resulting inter-node distances may degrade the performance of the network. This paper proposes an enhanced deployment algorithm based on Artificial Bee Colony (ABC). The ABC-based deployment is guaranteed to extend the lifetime by optimizing the network parameters and constraining the total number of deployed relays. Simulations validate the effectiveness of the proposed strategy under different cases of problem complexity. Results show that the proposed approach improves the network lifetime considerably when compared to solutions reported in the literature such as Shortest Path 3-D grid Deployment (SP3D) algorithm. HighlightsA novel energy efficient optimal deployment strategy is proposed in this work.The algorithm uses Artificial Bee Colony to optimize the network parameters.Lifetime enhancement is assured while cost and connectivity constraints are satisfied.The proposed strategy is benchmarked with an existing solution called the SP3D.Numerical results show the efficacy of the proposed deployment strategy.

Optimal Power Flow Using Differential Search Algorithm

Abstract —In this article, a new nature-inspired metaheuristic technique called the differential search algorithm is proposed to solve the optimal power flow problem. The proposed differential search algorithm has been developed and tested under normal and contingency power system conditions. To show the effectiveness of the proposed method, it has been demonstrated on the standard IEEE 30-bus and IEEE 118-bus test systems with different objectives that reflect performance indices of the power system. Obtained results using the proposed technique indicate that the proposed differential search algorithm provides an effective, a robust, and a high-quality solution for the optimal power flow problem. The comparisons of the proposed differential search algorithm results with those reported in the literature reveal the potential and superiority of the proposed algorithm in terms of the optimal solution quality and robustness.

Multiobjective Evolutionary Finance-Based Scheduling: Entire Projects’ Portfolio

A strength Pareto evolutionary algorithm (SPEA) is proposed and was modified by incorporating logic-preserving crossover and mutation operators and employed to devise a set of optimum finance-based schedules of multiple projects being implemented simultaneously by a construction contractor. The problem involves the minimization of the conflicting objectives of financing costs, duration of the group of projects, and the required credit. The modified SPEA was employed to obtain the Pareto-optimal fronts for the two-objective combinations as well as the three objectives. In addition, a fuzzy-based technique was used to help the contractors select the best compromise solution over the Pareto-optimal solutions. The proposed approach has been developed and implemented on projects with different sizes. The results obtained by the modified SPEA, fuzzy-based approach demonstrated its potential and effectiveness in finance-based scheduling of multiple projects.

A fuzzy logic feedback filter design tuned with PSO for L 1 adaptive controller

No simple way of tuning L1 adaptive controller feedback filter exists.Propose a Fuzzy-logic based approach for on-line tuning of the filter.Particle Swarm Optimization (PSO) is used to optimize the filter.Class of a strictly proper low pass filters with fixed structure is considered.Simulation demonstrate simplicity excellent performance and robustness. L 1 adaptive controller has been recognized for having a structure that allows decoupling between robustness and adaption owing to the introduction of a low pass filter with adjustable gain in the feedback loop. The trade-off between performance, fast adaptation and robustness, is the main criteria when selecting the structure or the coefficients of the filter. Several off-line methods with varying levels of complexity exist to help finding bounds or initial values for these coefficients. Such values may require further refinement using trial-and-error procedures upon implementation. Subsequently, these approaches suggest that once implemented these values are kept fixed leading to sub-optimal performance in both speed of adaptation and robustness. In this paper, a new practical approach based on fuzzy rules for online continuous tuning of these coefficients is proposed. The fuzzy controller is optimally tuned using Particle Swarm Optimization (PSO) taking into accounts both the tracking error and the controller output signal range. The simulation of several examples of systems with moderate to severe nonlinearities demonstrate that the proposed approach offers improved control performance when benchmarked to L 1 adaptive controller with fixed filter coefficients.

Fuzzy controller design using evolutionary techniques for twin rotor MIMO system: a comparative study

This paper presents a comparative study of fuzzy controller design for the twin rotor multi-input multioutput (MIMO) system (TRMS) considering most promising evolutionary techniques. These are gravitational search algorithm (GSA), particle swarm optimization (PSO), artificial bee colony (ABC), and differential evolution (DE). In this study, the gains of four fuzzy proportional derivative (PD) controllers for TRMS have been optimized using the considered techniques. The optimization techniques are developed to identify the optimal control parameters for system stability enhancement, to cancel high nonlinearities in the model, to reduce the coupling effect, and to drive TRMS pitch and yaw angles into the desired tracking trajectory efficiently and accurately. The most effective technique in terms of system response due to different disturbances has been investigated. In this work, it is observed that GSA is the most effective technique in terms of solution quality and convergence speed.

Design of robust PSS in multimachine power systems using backtracking search algorithm

This paper proposes a new approach for the simultaneous stabilization of multi-machine power system networks by tuning the parameters of power system stabilizers (PSS) using backtracking search algorithm (BSA). To enhance system damping, damping ratio based objective function is considered and widely used conventional lead-lag type PSS structure is used. The ability to lead the optimal design of the PSS regardless of the initial guess proves the robustness of the algorithm. The performances of the approach are investigated for two different multi-machine networks subjected to three phase fault and the results obtained from simulation verify the effectiveness of the proposed technique. The simulation results are also compared with a well-established swarm intelligence paradigm in this field called particle swarm optimization (PSO), which also gives confidence on the proposed technique.

Enhanced Trade-Off of Construction Projects: Finance-Resource-Profit

AbstractThe parameters of finance requirements, resource leveling, and anticipated profit have significant influence on many aspects of project management. These parameters interact and occasionally conflict with each other. Accordingly, achievement of a balance between these three parameters is crucial to ensure the accomplishment of project objectives. A multi-objective multimode scheduling optimization algorithm is proposed to establish the optimal trade-off between these three parameters. The strength Pareto evolutionary algorithm (SPEA) was implemented to obtain the solutions comprising the Pareto-optimal trade-off. The developed SPEA was validated by reproducing identical results of a time/cost trade-off problem in the literature. The developed SPEA was used to obtain the Pareto-optimal trade-off of a network of nine multimode activities that comprised fifty solutions. The trade-off of fifty solutions allows decision makers explore the impact of finance upon the efficiency of resource utilization an...

An efficient approach for parameter estimation of PV model using DE and fuzzy based MPPT controller

Photovoltaic (PV) panel can be represented by an equivalent electric circuit where the major problem with this PV model is the determination of the model parameters. In this paper, PV parameter estimation problem is converted to an optimization problem where Differential Evolution (DE) as an efficient optimizing technique is employed to estimate the model parameters at standard test condition (STC) (1000 W/m2 and 25°C) using the data provided by the manufacturer. A complete equivalent electric circuit model of the PV panel is developed in the MATLAB/Simulink and estimated parameters values are verified by comparing the determined I-V curves with the experimental data given in the data sheet. The developed PV model has been examined under different operating conditions and its accuracy has been verified. An efficient maximum power point tracking (MPPT) controller based on the Fuzzy Logic is also proposed. Analysis and comparison shows that the FLC based controller is more efficient and overcome the shortcomings of the conventional methods.

Computational Intelligence in Smart Grids: Case Studies

This chapter briefly provides an overview of related work on computational intelligence techniques in smart grids. It also reviews two computational intelligence techniques and some of their current applications in solving problems associated with smart grids implementation and deployment. More importantly, two case studies are presented and intensively discussed. These applications include parameter estimation of photovoltaic models and tracking of maximum power point. This chapter also highlights some open research problems and directions for future research work.

Direct control of three-phase smart load for neutral current mitigation

Electric Spring (ES), a power electronic based device, has been recently developed to improve various attributes of future power systems with high penetration of intermittent renewable energy sources. ES is connected in series with non-critical load to form an adaptive load, known as smart load. This configuration has been successfully used to mitigate voltage and frequency fluctuations, ensure demand side management, and improve power quality. It has also been used to reduce three-phase power imbalance and so the adverse effect of neutral current is eliminated. In this work a novel control scheme, based on run time impedance measurement, is proposed to mitigate neutral current from unbalanced three-phase system. Mathematical analysis is also given and corresponding simulations are carried out to substantiate the theoretical framework. Simulation results show the efficacy of presented technique under various unbalanced loading conditions.