Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Hybrid Genetic Algorithms for Telecommunications Network Back-Up Routeing
BT Technology Journal
IEEE Transactions on Evolutionary Computation
Generating robust and flexible job shop schedules using genetic algorithms
IEEE Transactions on Evolutionary Computation
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Power system state estimation is aimed at providing modern electric control centers with accurate and reliable real-time databases. In the past few years, many papers have been published on state estimation applied to electric power systems. Different algorithms-static, tracking and dynamic-have been proposed aside with a variety of applications: detection and identification of bad data, network configuration, etc. With the development of installed capacity and power system interconnect scale, the importance of stable monitor and control is becoming more and more obvious. The phase angle is one of basic state variable of electric power system, so monitoring and controlling power angle and phases of the bus voltage are the most important means. A real-time phase angle can be applied to state estimate, static monitor, dynamic stable prediction and control, and adaptive out-of step relay protection. Because of the complexity and expense of phase angle measurement engineers often tend to minimize the amount of phase angle measurement. This paper applies an adaptive and heuristic genetic algorithm to optimize the phase angle measurement unit placement so that the placement spot is minimal and the power system can be observed, with introducing heuristic search into genetic algorithm so as to increase speed of searching optimal solution. And simulation is evaluated on IEEE 22-bus power system.