Abstract
In a photovoltaic power plant (PVPP), the DC-AC converter (inverter) is one of the components most prone to faults. Even though they are key equipment in such installations, their fault detection techniques are not as much explored as PV module-related issues, for instance. In that sense, this paper is motivated to find novel tools for detection focused on the inverter, employing machine learning (ML) algorithms trained using a hybrid dataset. The hybrid dataset is composed of real and synthetic data for fault-free and faulty conditions. A dataset is built based on fault-free data from the PVPP and faulty data generated by a digital twin (DT). The combination DT and ML is employed using a Clarke/space vector representation of the inverter electrical variables, thus resulting in a novel feature engineering method to extract the most relevant features that can properly represent the operating condition of the PVPP. The solution that was developed can classify multiple operation conditions of the inverter with high accuracy.

Abstract
This paper describes the newly developed Vector Control Signal Processing (VCSP) blockset for use with Matlab(R) and Simulink(R). The originality of this blockset consists on the extension of Simulink for design, simulation and prototyping of signal processing algorithms in motion control systems. This blockset is the first know collection of Simulink blocks to bridge the gap between digital algorithm development and subsequent implementation in motion control systems. The VCSP blockset together with Real-Time Workshop uses the inherent visual programming techniques of Simulink and a number of pre-built blocks to reach the above goals. Due to its open and flexible nature this approach is also very useful as a tool for teaching. This paper is focused on modelling and simulation of motion control systems, in particular employing rotating AC machines and vector control methods. The basic of blockset functions and some examples of modelling techniques for simple drive and complex drive structures are presented, Simulations results are also presented and discussed.

Abstract

Abstract

Abstract
Sliding mode controllers are derived for the control of the average output voltage in DC/DC power converters. The controller design is carried out on the basis of well-known bilinear models of such circuits. A cascaded control structure is chosen for ease of control realization and to exploit the motion separation property of this power converter. The performance of the proposed sliding mode controllers is tested for the buck and boost converter type. The numerical simulations will demonstrate the efficiency of sliding mode techniques in this field as a powerful alternative to other existing methods. © 2003 IFAC.

Abstract
A new online identification methodology for estimation of the rotor flux components and the main electrical parameters of vector controlled induction motors is presented in this paper. The induction motor model is referred to the rotor reference frame for estimation of rotor flux and rotor parameters, and referred to the stator reference frame to estimate stator parameters. The stator parameters estimation is achieved by a prediction error method based on a model structure described by a linear regression that is independent of rotor speed and rotor parameters. The rotor flux components and rotor parameters are estimated by a reduced order extended Kalman filter, using a 4th-order state-space model structure where the state equation is described by matrices that are diagonal and independent of rotor speed as well as stator parameters. Both methods work in a boot-strap manner. © 2003 IEEE.