Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed.Index Terms-Classical optimization, particle swarm optimization (PSO), power systems applications, swarm intelligence.

This paper investigates the experimental implementation and detection of rotor faults in permanent magnet synchronous machines. Methods are shown how to experimentally introduce static and dynamic eccentricities and broken magnet cases. A new magnet flux estimation that does not require the measurement of the rotor position or speed is developed. The detection of these and other rotor faults by measuring only the stator currents and voltages are shown experimentally. The paper concludes by contributing a description of a condition-monitoring scheme for detection rotor faults. Index Terms-Permanent magnet synchronous machines (PMSM).

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