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The paper presents an original self-tuning strategy for a minimum variance controller used to control highly disturbed processes. The role of the proposed algorithm is to implement a self-protection mechanism against major disturbances produced by measurable perturbations acting on a controlled plant. By a variable setting of the control penalty factor (ρ), which weights the influence of the control variance in a cost criterion function, the main objective is to maintain the stability of the control system, even under action of high perturbations. This new self-tuning strategy is based on the assumption that the disturbances acting on the controlled plant are measurable, being implemented and tested for the case of an induction generator integrated into a wind energy conversion system. The power control system's main goal is to reject the disturbance generated by a mechanical torque variation, due to variable wind speed, acting as an external measurable perturbation. Depending on the variation range of this mechanical torque (small variations corresponding to a relatively stationary regime or high variations due to wind gusts), the proposed algorithm sets the control penalty factor to different values. As a result, the overall effect of the self-tuning strategy will be to extend the operating range of the wind aggregate, by maintaining the stability of the control system (strongly disturbed by high wind gusts) and also by an efficient disturbance rejection. As a novelty, the base rule of the self-tuning strategy proposes a rectangular pulse variation of the control penalty factor when a high disturbance occurs. By setting a high amplitude for this pulse variation of ρ, the control is strongly limited, thus ensuring the system stability. At the end of this pulse with a suitable width, the effect of disturbance is fast rejected by returning to a small stationary value of ρ, which allows high enough control. Although designed and validated for a particular case, this self-tuning strategy is generally valid for the minimum variance control of any other process disturbed by measurable perturbations.

Self-balancing vehicles represent an interesting topic to be treated in future means of transport. They can have the advantages of being small, practical, easy to carry and to use. The main goal of the article is the study and analyze of a PID classic controller on two-wheel experimental robot. The main objective is to maintain a vertical position with low angle deviations. The proposed system uses an accelerometer and a gyroscope module in order to obtain position and angle velocity, respectively stepper motors for robot driving.

This paper describes a SCADA system for a microgrid laboratory that simulates a grid-connected wind turbine. The SCADA application monitors the entire system, ensuring reliable and stable operation. It gathers information from all the elements of the system, including the embedded controller of the emulator and the grid-connected inverter. It can monitor real-time parameters such as voltages, currents, or powers, and can store measurements in a MySQL database. This system also contains an interface for remote viewing designed using LabVIEW Remote Panels embedded into a web page. In order to tighten the security features on the website, the default web server provided by NI is replaced with WAMP server, making it possible to control the content of the website using server-side technology. Only authorized users can view the SCADA application or access the database for historical data.

This paper presents a stability and performance analysis of a self-tuning minimum variance control system. Designed through a cost function minimization, the control law is described by a linear difference model with time varying parameters. Based on a linearized process model around an operating point and by using a parameter estimator, the control system automatically adapts itself when process parameters change (as effect of a disturbance). However, the performance of the control system is strongly conditioned by an a priori setting of a factor that weights the control variance term of the cost function. The goal of this stability analysis is to provide a strategy regarding how to tune this control penalty factor, which significantly influences the stability and performance of the control system. Two approaches were considered: one based on the control system response in relation to the disturbance, validated by a second one based on frequency response analysis.

Microgrids represent a means of improving the conventional electrical grid network, making it more reliable, secure, cooperative, efficient and, especially cleaner. This paper presents the management of a hierarchical DC-coupled microgrid with distributed architecture implemented in a microgrid laboratory containing two renewable sources, namely wind and hydro. Power supply management strategies are applied in order to supply a critical, non-dispatchable load. Due to effective management, the microgrid is robust and capable of handling most of the problems that are associated with renewable energies.