A Novel Method to Estimate the Rotor Resistance of the Induction Motor Using Support Vector Machines

Villazana, Sergio; Caralli, Antonino; Seijas, César; Villanueva, Carlos

doi:10.1109/iecon.2006.347340

Cited by 5 publications

(6 citation statements)

References 9 publications

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“…The purpose of the validation studies is not to claim that SVM-based method is superior to other algorithms but to show that it is able to deliver reasonable results. The SVM method has been effectively employed to solve problems in different power system areas such as transient stability [25], power quality [27] and electrical machine parameter estimation [28]. Table III shows similar results considering that the input data includes only 6 buses instead of 25 buses and considering the same 80 unseen scenarios used for Table II.…”

Section: Resultsmentioning

confidence: 99%

“…The problem of choosing an architecture for a neural network is replaced here by the problem of choosing a suitable kernel for the SVM, as detailed in the following subsections [22][23][24]. In fact, this aspect has allowed the development of somewhat fast training techniques, even with a large number of input variables and big training sets, which is particularly suitable for large power system analysis [25][26][27][28].…”

Section: Support Vector Machinesmentioning

confidence: 99%

“…One of the benefits of this approach is that for every pair of classes, we deal with a much smaller optimization problem if compared with the "one-against-all" approach. Given that QP optimization algorithms used for SVMs are polynomial to the problem size, such a reduction can yield substantial savings in the total computational time [26][27][28][29].…”

Section: Multiclass Svmmentioning

confidence: 99%

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Integrated volt/Var control in modern distribution power systems based on support vector machines

Salles

Pinto

Freitas

2016

Int. Trans. Electr. Energ. Syst.

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SUMMARYIntegrated volt/Var control (IVVC) is an important tool to optimize voltage and VAR resources and improve energy efficiency and, therefore, plays an important role to support the smart grid vision. This paper investigates the application of support vector machines (SVMs) to control switched capacitor banks and voltage regulators of distribution systems in order to minimize system losses and maintain acceptable voltage profile. The objective is to develop an approach that can use voltage/current measurements provided by active and reactive monitors to IVVC control without the need to perform retraining or repeat optimization simulations. The performance of the SVM scheme for different input selection (i.e., active and reactive power and node voltage) is discussed. Moreover, the generality of the proposed SVM scheme is investigated under noisy environment as well as under network reconfiguration. Studies on a typical distribution system show that the proposed approach has been able to achieve satisfactory results.

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Section: Resultsmentioning

confidence: 99%

Section: Support Vector Machinesmentioning

confidence: 99%

Section: Multiclass Svmmentioning

confidence: 99%

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Integrated volt/Var control in modern distribution power systems based on support vector machines

Salles

Pinto

Freitas

2016

Int. Trans. Electr. Energ. Syst.

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“…The parameters C and ε will be set by designer during training step for optimizing slack variables [25]. To calculate the parameters of w and b,, Eq.…”

Section: Support Vector Regressionmentioning

confidence: 99%

“…By multiplying the support vectors to the kernel K (X i , X ), the output provide errors equal, less or greater than ε. Kernel function is equal to vectors X i and X j in the feature space as φ(X i ) and φ X j where K X i , X j φ(X i ) * φ X j . Therefore, the training of the SVR can solve a quadratic and convex optimization problem [25].…”

Section: Support Vector Regressionmentioning

confidence: 99%

Estimating power factor of induction motors at any loading conditions using support vector regression (SVR)

2018

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Three-phase induction motors are industrial work horses known as inductive loads. Inductive loads always create low power factor due to consuming more reactive power. Low power factor not only makes a penalty charge for costumers, but also produces energy losses in electrical systems. To prevent these problems, the power factor which is the ratio of active and reactive power must be maintained toward unity. The power factor can be controlled by manipulating either of active or reactive power. In induction motors, active power is proportional to the motor load that variation of motor load results in increase or decrease in power factor. However, adding reactive power by capacitors would be a substantial solution to improve and control the power factor in unity. Many researches expressed that injecting improper reactive power to improve the power factor creates under-and over-correction. To prevent such problems, equation of power factor correction can be employed in order to obtain the optimal value of reactive power. In this equation, the presence of power factor at every single loading point is required. Estimation techniques can be a significant key to determine the power factor at every loading point. In this paper, several statistical methods including, kriging, regression, artificial neural network and support vector regression, are tested in three induction motors. A comparison is presented to verify the great performance of support vector regression method.

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High performance direct vector control of an asynchronous motor based on EKF

Shen

Xiao-ping³

2010

2010 5th IEEE Conference on Industrial Electronics and Applications

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A Novel Method to Estimate the Rotor Resistance of the Induction Motor Using Support Vector Machines

Cited by 5 publications

References 9 publications

Integrated volt/Var control in modern distribution power systems based on support vector machines

Integrated volt/Var control in modern distribution power systems based on support vector machines

Estimating power factor of induction motors at any loading conditions using support vector regression (SVR)

High performance direct vector control of an asynchronous motor based on EKF

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