Camila Paes Salomon

Induction Motor Efficiency Evaluation Using a New Concept of Stator Resistance

This paper presents an air-gap torque (AGT)-based method for efficiency estimation of induction motors. A new concept of stator resistance that includes the mechanical losses effect is proposed. This new stator resistance is estimated through a particle swarm optimization approach based on the stator flux equations and minimization of torque error at the rated operation point. Then, the obtained stator resistance is used in the AGT equations to estimate the shaft torque and then the efficiency. Moreover, the rotor speed is estimated using induction motor current signature analysis. Thus, the proposed methodology for induction motor efficiency estimation relies only on line currents, line voltages, and nameplate data, being appropriate for in-service applications. Finally, the simulation and experimental results are presented to validate the proposed method at different load conditions.

Particle Swarm Optimization Applied to Restoration of Electrical Energy Distribution Systems

This paper presents a technical application of the Particule Swarm Optimization - PSO technique to a reconfiguration problem of a electrical energy distribution system. The proposed methodology consists of the use of the maximization function of the number of loads supplied and the loss minimization by the expansion of the original PSO. The approach utilized the Distribution_System_01. A number of tests were carried out in the system to simulate several fault occurrences in the electrical energy transmission lines. The PSO algorithm encountered the optimal solution in a reasonable CPU time, compared to the dimension of the distribution system.

Load flow computation via Particle Swarm Optimization

This paper proposes the application of a Particle Swarm Optimization (PSO) algorithm to the Load Flow calculation in Power Systems. The proposed methodology is based on the minimization of power mismatches in the system buses. The power flow study provides the system status in the steady-state and it is fundamental to the power system operation, planning and control. PSO is applied in a new computational model for the system power flow obtainment. This model searches for a better convergence, as well as a wider application in comparison with traditional methods as the Newton-Raphson method. This methodology was tested with numerical experiments accomplished in an IEEE 6-Bus System.

A stator flux synthesis approach for torque estimation of induction motors using a modified stator resistance considering the losses effect

This work proposes a methodology for induction motor torque estimation based on the stator flux model. The presented methodology just relies on line voltages, line currents and nameplate data; and it adopts a modified stator resistance, which already comprises the mechanical losses effect. This modified stator resistance is estimated through a Particle Swarm Optimization (PSO) algorithm, which is a kind of artificial intelligence applied to optimization problems. The PSO presented objective function aims to minimize the torque error at the rated operation point. Simulation and experimental results validate the effectiveness of the presented methodology.

A system for turbogenerator predictive maintenance based on Electrical Signature Analysis

This paper proposes a prototype to detect incipient faults in non-salient pole synchronous generators based on the Electrical Signature Analysis (ESA) technique. The methodology consists in taking measurements of the machine stator voltages and currents and processing the acquired signals. By analyzing the voltages and currents spectra, it is possible to distinguish a faulty condition from a healthy one and to infer the failure type of the generator. This is summarized in a set of ESA failure patterns, which were proved through tests conducted in a custom made scale model laboratory. The custom laboratory setup consists of a small fault injection capable two-pole synchronous generator driven by an inverter-fed induction motor. The proposed prototype is operating in four turbogenerators of a Brazilian power plant. The main advantages of the presented system are the low intrusiveness, the ease of installation and it is economically viable. Moreover, the ESA failure patterns are based on defined frequencies and the structural features of the machine.

Differential Evolution based Air-Gap Torque method approach for induction motor efficiency estimation

This paper proposes a methodology for torque and efficiency estimation of in-service induction motors based on the Air-Gap Torque (AGT) method. The proposed methodology only relies on induction motor stator voltages, currents and nameplate data. A modified stator resistance is estimated including the mechanical losses effect. This modified stator resistance is estimated by using a Differential Evolution (DE) based algorithm. The DE procedure estimates the modified stator resistance by using the stator flux equations, minimizing the torque error at the rated operation point. The estimated stator resistance is applied in the AGT method equations to compute the induction motor torque and efficiency. Simulation tests were performed to validate the proposed methodology.

An air-gap torque based method for efficiency evaluation using pso to estimate a new concept of stator resistance including the losses effect

This work proposes an air-gap torque (AGT) based method for efficiency and torque estimation of induction motors. A new concept of stator resistance including the mechanical losses effect is presented. This modified stator resistance is estimated through a Particle Swarm Optimization (PSO) approach. The proposed methodology only relies on line currents, line voltages and nameplate data, which is appropriate for in-service applications. Finally, simulation and experimental tests were conducted to validate the proposed method at different load conditions.

Comparison between PSO and GA in System Restoration Solution

The use of the Evolutionary Computation (EC) grew in interest recently. Among various Evolutionary Computation approaches, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used in optimization problems; they have much in common but also have some differences. This paper presents a decision support tool based on Particle Swarm Optimization Technique (PSO) and Genetic Algorithm Technique (GA). This tool is applied to electrical power system restoration after an incident. The operator support systems play an important role in a performance of the complex process involving decision-making problems of combinatory nature. The techniques are based on the change of system functional configuration and consist in the use of the maximization of power demand supplied and minimization of the number switched lines. These techniques also avoid the overload of system lines. A case study is introduced.

Discrimination of Synchronous Machines Rotor Faults in Electrical Signature Analysis Based on Symmetrical Components

Electrical signature analysis (ESA) has been successfully applied to predictive maintenance of synchronous machines. The fault diagnosis is performed by analyzing failure patterns in the current or voltage spectra, which allow the discrimination of a healthy and a faulty condition on the monitored machine. Generally, the failure patterns are a function of the line frequency, rotor rotation frequency, and some structure features of the machine. The rotor rotation frequency pattern, for instance, is indicative of rotor mechanical problems and rotor winding interturn short-circuit. An increase of this frequency component magnitude may indicate the incipience of a rotor fault but may not discriminate the nature of the fault (electrical or mechanical). Thus, it is necessary to distinguish the effects of electrical and mechanical faults in these components in order to get a reliable diagnosis of the machine. No works have been found in the literature approaching this specific issue. This paper proposes a simple and innovative methodology to distinguish the effect of electrical and mechanical faults in the mentioned ESA failure patterns based on the method of symmetrical components. Its effectiveness is validated by experiments performed on a synchronous generator test rig. The proposed condition monitoring system is simple, low cost, and low intrusive, as it only relies on stator electrical quantities. Moreover, it is in operation in a Brazilian 404-MW thermal power station.

Applications of Genetic Algorithm in Power System Control Centers

Electric power systems are the structures that have the purpose of generating, transporting and distributing electricity. These structures are composed of energy generating plants, transmission lines, distribution systems and elevating and distribution substations. The biggest challenge of this type of system is that the electrical energy is not a material that can be stored. That is, so it is required by the load (which can be a driver in an industry or the mere light of a lamp in a residence) this energy must be generated, transported and delivered to the user instantly.