## 1 Introduction
This results in the strong dependence of estimator operation on the control system performance. In [17], an estimator was implemented in stationary reference frame (\(\alpha\)-\(\beta\)), therefore it was based only on measurable signals--independently of the internal signals from a control scheme. In [18] Q-MRAS was proposed for speed estimation. Figure 1: General idea of MRAS type parameter estimator (**u**—input signals vector, \(x^{\prime\prime\prime}\)—reference value of chosen signal, \(x^{\prime\prime\prime}\)—adjustable (estimated) value of chosen signal, \(p^{est}\)—estimated parameter, \(\epsilon\)—error between reference and adjustable model). * Active power based (P-MRAS)--proposed in [19] for stator resistance estimation. Similarly to Q-MRAS, it is implemented only in the synchronous reference frame (_x-y_), which determines a strong reliance on the control structure performance. In [20] a semi-active power estimator is proposed to improve robustness on rotor resistance variation. In [21] it was used for rotor resistance estimation. * X based (X-MRAS)--proposed in [22] for stator resistance estimation. Here X is a fictitious quantity based on stator voltage and current. It was implemented only in a synchronous reference frame. It can be used for synchronous speed estimation [22]. * PY based (PY-MRAS)--proposed in [23] for stator resistance estimation. It relies on the active power and Y which is a fictitious quantity (based on stator voltage and current). The major benefit of this estimator is that the adjustable model is independent of the rotor speed. However, only simulation results were presented. * Electromagnetic torque (T-MRAS)--proposed in [21] for rotor resistance estimation. Its major shortcoming is the fact that both reference and adjustable models are non-measurable quantities. The reference model depends on the stator resistance. * Back electromagnetic force (back-EMF MRAS)--proposed in [18] for speed estimation. In [24] it was proposed for the stator resistance estimation. Its major advantage is that pure integration is not required through the implementation of the estimator. The reference model depends directly on rotor resistance. In [25], an improvement of estimator stability was proposed. However, the estimator employs an internal signal from the control structure (implemented in the (_x-y_) reference frame). To sum up, MRAS based estimators for induction motor parameters have been developed in the last few decades. As a result, it is possible to estimate the stator or rotor resistance of a machine. However, the research on a MRAS type estimator which enables the estimation of both resistances simultaneously is still insufficient. In [26], an F-MRAS type estimator was proposed for the simultaneous estimation of rotor and stator resistance, and also the estimation of motor rotational speed. The major drawback of the proposed approach is the fact that both (adjustable and reference) models are directly dependent on the estimated parameters. Therefore, incorrect estimation of at least one parameter may deteriorate the performance of the entire estimator. Additionally, both models of the proposed MRAS are not measurable signals, in consequence it is not possible to check how calculated signals correspond to their real values--estimated parameters are calculated using estimated signals. This article presents a new PQ-MRAS estimator which enables the estimation of both resistances simultaneously. It is based on the active and reactive powers of the induction motor. The main benefits of proposed estimator are that reference models are calculated using only measurable signals, such as current, voltage and rotor speed. In the paper, the estimator was tested for different conditions through computer simulations as well as experimental tests. It is necessary to point out that the approaches based on the Kalman Filter enable a simultaneous, highly accurate estimation of motor parameters and state variables [27; 28].