Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

The Theory of Island Biogeography

An attempt is made in This work to present critical literature review and an up-to-date and exhaustive bibliography on the AGC of power systems. Various control aspects concerning the AGC problem have been highlighted. AGC schemes based on parameters, such as linear and nonlinear power system models, classical and optimal control, and centralized, decentralized, and multilevel control, are discussed. AGC strategies based on digital, self-tuning control, adaptive, VSS systems, and intelligent/soft computing control have been included. Finally, the investigations on AGC systems incorporating BES/SMES, wind turbines, FACTS devices, and PV systems have also been discussed.

Recent philosophies of automatic generation control strategies in power systems

This paper presents a new multiobjective evolutionary algorithm for environmental/economic power dispatch (EED) problem. The EED problem is formulated as a nonlinear constrained multiobjective optimization problem. A new strength Pareto evolutionary algorithm (SPEA) based approach is proposed to handle the EED as a true multiobjective optimization problem with competing and noncommensurable objectives. The proposed approach employs a diversity-preserving mechanism to overcome the premature convergence and search bias problems. A hierarchical clustering algorithm is also imposed to provide the decision maker with a representative and manageable Pareto-optimal set. Moreover, fuzzy set theory is employed to extract the best compromise nondominated solution. Several optimization runs of the proposed approach have been carried out on a standard test system. The results demonstrate the capabilities of the proposed approach to generate well-distributed Pareto-optimal solutions of the multiobjective EED problem in one single run. The comparison with the classical techniques demonstrates the superiority of the proposed approach and confirms its potential to solve the multiobjective EED problem. In addition, the extension of the proposed approach to include more objectives is a straightforward process.

Environmental/economic power dispatch using multiobjective evolutionary algorithms

The potential and effectiveness of the newly developed Pareto-based multiobjective evolutionary algorithms (MOEA) for solving a real-world power system multiobjective nonlinear optimization problem are comprehensively discussed and evaluated in this paper. Specifically, nondominated sorting genetic algorithm, niched Pareto genetic algorithm, and strength Pareto evolutionary algorithm (SPEA) have been developed and successfully applied to an environmental/economic electric power dispatch problem. A new procedure for quality measure is proposed in this paper in order to evaluate different techniques. A feasibility check procedure has been developed and superimposed on MOEA to restrict the search to the feasible region of the problem space. A hierarchical clustering algorithm is also imposed to provide the power system operator with a representative and manageable Pareto-optimal set. Moreover, an approach based on fuzzy set theory is developed to extract one of the Pareto-optimal solutions as the best compromise one. These multiobjective evolutionary algorithms have been individually examined and applied to the standard IEEE 30-bus six-generator test system. Several optimization runs have been carried out on different cases of problem complexity. The results of MOEA have been compared to those reported in the literature. The results confirm the potential and effectiveness of MOEA compared to the traditional multiobjective optimization techniques. In addition, the results demonstrate the superiority of the SPEA as a promising multiobjective evolutionary algorithm to solve different power system multiobjective optimization problems.

Multiobjective evolutionary algorithms for electric power dispatch problem

Load frequency control strategies: A state-of-the-art survey for the researcher

Traditionally electric power systems are operated in such a way that the total fuel cost is minimized regardless of emissions produced. With increased requirements for environmental protection, alternative strategies are required. This paper presents a summary of algorithms of environmental-economic dispatch in electric power systems since 1970. The algorithms attempt to reduce the production of atmospheric emissions such as NO/sub x/ and SO/sub x/ caused by the operation of fossil-fueled thermal generation. Such reduction is achieved by including emissions either as a constraint or as a weighted function the objective of the overall dispatching problem. >

A summary of environmental/economic dispatch algorithms

An adaptive fuzzy gain scheduling scheme for conventional PI and optimal load frequency controllers has been proposed. A Sugeno type fuzzy inference system is used in the proposed controller. The Sugeno type fuzzy inference system is extremely well suited to the task of smoothly interpolating linear gains across the input space when a very nonlinear system moves around in its operating space. The proposed adaptive controller requires much less training patterns than a neural net based adaptive scheme does and hence avoiding excessive training time. Results of simulation show that the proposed adaptive fuzzy controller offers better performance than fixed gain controllers at different operating conditions.

Adaptive fuzzy gain scheduling for load frequency control

Optimal coordination of directional overcurrent relays (DOCRs) is a highly constrained and nonlinear optimization problem. The operating time of each relay depends on two independent variables called plug setting and time multiplier setting. As the network becomes larger and more complex, the number of relays will increase and, thus, finding the optimal solution becomes very hard. In this paper, a new population-based evolutionary algorithm called biogeography-based optimization (BBO) is proposed and the performance of ten types of constraint-handling techniques is evaluated. In addition, a new hybrid BBO with linear programming (BBO-LP) is proposed to enhance the performance of the conventional BBO algorithm. The performance of the proposed BBO-based algorithms is evaluated by using five test systems. The results show the effectiveness and superiority of the proposed algorithms compared with the performance of the other optimization methods presented in the literature.

Optimal Coordination of Directional Overcurrent Relays Using Biogeography-Based Optimization Algorithms

Optimal power system stabilizers for multi machine systems

Biogeography-based optimization (BBO) is a new population-based evolutionary algorithm and is based on an old theory of island biogeography that explains the geographical distribution of biological organisms. BBO was introduced in 2008 and then a lot of modifications and hybridizations were employed to enhance its performance. The researchers found that the original version of BBO has some weakness on its exploration. This paper tries to solve the root problems itself instead of solving its effect by using different techniques. It proposes two modifications; firstly, modifying the probabilistic selection process of the migration and mutation stages to give a fairly randomized selection for all the features of the islands. Secondly, the clear duplication process, which is located after the mutation stage, is sized to avoid any corruption on the suitability index variables of the non-mutated islands. The proposed modifications are extensively tested on 120 test functions with different dimensions and complexities. The results proved that the BBO performance can be enhanced effectively without embedding any additional sub-algorithm, and without using any complicated form of the immigration and emigration rates. In addition, the new BBO algorithm requires less CPU time and becomes even faster than the original simplified partial migration-based BBO. These essential modifications have to be considered as an initial step for any other modifications.

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Jawad Talaq

Papers

A summary of environmental/economic dispatch algorithms

Adaptive fuzzy gain scheduling for load frequency control

Optimal Coordination of Directional Overcurrent Relays Using Biogeography-Based Optimization Algorithms

Optimal power system stabilizers for multi machine systems

Solving the associated weakness of biogeography-based optimization algorithm