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A new solution for VAR planning in distribution systems is proposed. It is based on genetic algorithms (GAs). Allelic alphabet and Hamming distance concepts are efficiently exploited under real coding. The model's robustness is demonstrated through a 69-node system. The effect of the genetic operators is quantified (mutation probability and mutation radius, crossover probability) regarding the convergence...
This paper reviews the application of some evolutionary computation techniques for power market equilibrium determination. Competitive power markets are formulated as a Cournot game. Genetic algorithm and coevolutionary computation techniques are adapted to numerically solve the optimization problem of finding the market equilibrium. Numerical example shows that the evolutionary computation approach...
In this paper the genetic algorithm of Chu and Beasley (GACB) is applied to solve the static and multistage transmission expansion planning problem. The characteristics of the GACB, and some modifications that were done, to efficiently solve the problem described above are also presented. Results using some known systems show that the GACB is very efficient. To validate the GACB, we compare the results...
This paper compares a method of designing power system stabilizer for a multimachine power system using a simple evolutionary algorithm called population-based incremental learning (PBIL) and genetic algorithms (GAs). The controller design issue is formulated as an optimization problem that is solved via PBIL algorithm and GAs. The resulting controllers are tested on both the nominal and off-nominal...
This paper studies the design of reactive power supply for microgrids. The problem can be stated as follows: for a given microgrid, determine an optimum allocation of reactive power sources so that adequate voltages can be maintained during islanded mode as well as more than one topology of islanded operation. A genetic algorithm is used to develop the optimal allocation
The provision of un-interrupted power supply for all customers has always been one of the fundamental concern of maintenance scheduling. The maintenance scheduling (MS) is characterized as a constrained optimization problem. Combined genetic algorithm and simulated annealing (CGASA) are proposed in this paper for reliable preventive unit maintenance scheduling (PUMS). This approach is used to find...
For further benefits in using dispersed generation (DG) in electrical power system (EPS) in both normal and critical operating situations, new equipments required for investment in distribution networks are recommended. This paper presents an optimization combination approach based on knapsack problem and dynamic programming to determine a range of minimal number of specific needed switches and their...
Summary form only given. This work presents a methodology for designing optimal metering systems for real-time power system monitoring, taking into account different topologies that the network may experiment. Genetic algorithms are employed to achieve a trade-off between investment costs and reliability of the state estimation process under many different topology scenarios. This is done by formulating...
Market-based transmission expansion planning gives information to investors on where is the most cost efficient place to invest and brings benefits to those who invest in this grid. However, both market issue and power system adequacy problems are system planers' concern. In this paper, a hybrid probabilistic criterion of expected economical loss (EEL) is proposed as an index to evaluate the systems'...
This article describes a way of designing a hybrid system for short-term load forecasting, integrating rough sets theory with fuzzy neural networks using a multi-objective genetic algorithm. The multi-objective genetic algorithm is used to automatically learn the knowledge of historical data and find the best factors that are relevant to electric loads. The concept of entropy is introduced to describe...
A multi-objective programming procedure is used for solving the problem of optimal allocation of flexible AC transmission systems (FACTS) devices in a power system. The evolutionary approach consists of a multi-objective genetic algorithm (MOGA), which is used to characterize the Pareto optimal frontier (non-dominated solutions) and to provide to decision makers and engineers insightful information...
This paper presents an adaptive fuzzy logic power system stabilizer (FLPSS). A two-stage technology of FLPSS adaptation is considered taking into account real conditions in a bulk electric power system. A genetic algorithm (GA) is applied for tuning parameters of FLPSS. An artificial neural network (ANN) is used on-line to adapt the FLPSS to changes in operating conditions
Summary form only given. Stabilizing the long-term electricity markets by providing new generation resources, is one of the most important challenges that are surfaced to the industry regulators. To deal with this complex problem, this paper proposes a comprehensive multiple attribute decision making (MADM) framework, in which the genetic algorithm (GA) is used to model the investment decisions of...
Developments in power plant control are increasing steadily in recent years by seeking new techniques other than conventional PID controls. This panel introduces intelligent techniques to power plant control, which deal with complex dynamic systems having significant uncertainties. As for intelligent techniques, neural network (NN), fuzzy logic (FL), evolutionary programming (EP), genetic algorithm...
This paper describes an approach for coordinated tuning of AVRs and PSSs based on meta-heuristics. Two methods, based on small variations of the standard genetic algorithms, are described. The basic difference between the methods is in the way they evaluate the fitness function of the multiobjective problem. Results obtained in the traditional New England power system indicated that the methodology...
Power system stability enhancement via coordinated design of power system stabilizers (PSSs) and STATCOM-based damping stabilizers is thoroughly investigated in this paper. This study presents a singular value decomposition (SVD) based approach to assess and measure the controllability of the poorly damped electromechanical modes by different control inputs. The coordination among the proposed damping...
This paper addresses some of the modeling and economic issues pertaining to the optimal reactive power planning of radial distribution systems with distributed generation. When wind power generation (WPG) units are installed in a distribution system, they may cause reverse power flows and voltage variations due to the random-like outputs of wind turbines. To solve this problem, we introduce static...
Capacitor setting/switching and network reconfiguration are two important means for optimizing the operating condition of the distribution systems. For both of them are complicated combinatorial algorithms, it is hard to effectively combine these two important means to do better optimization. In this paper, a joint optimization algorithm, based on the combination of capacitor switching and network...
This paper presents an efficient real-coded mixed-integer genetic algorithm (MIGA) for solving non-convex optimal power flow (OPF) problems with considering transmission security and bus voltage constraints for practical application. In the MIGA method, the individual is the real-coded representation that contains a mixture of continuous and discrete control variables, and two arithmetic crossover...
One advantage of multi-objective genetic optimization algorithms over classical approaches is that many non-dominated solutions can be simultaneously obtained by their single run. In this paper, we proposed a fuzzy rule-based classifier for electrical load pattern classification by using multi-objective genetic algorithm and fuzzy association rule mining. Multi-objective genetic algorithm is used...
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