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Sensors calibration plays a crucial role in controlling systems and achieving fault-tolerant control by ensuring accuracy, performance, safety, energy efficiency, and compliance with standards. It is an essential to maintain the reliability and effectiveness of modern control systems across various applications. In this paper, we represent a new algorithm that processes a set of raw data collected by a sensor to find the mapping function that relates the raw data to the real value of the measured signal by the sensor. Working on sensors with an unknown mapping function, unknown parameters, or with external disturbances, that affects their behaviour, represents a problem; moreover, it takes a lot of time and effort to calibrate the sensor before each use. Several techniques were used to overcome these aspects mostly by recording the output of the sensor for different input values that change manually, to calibrate the sensor. However, the represented technique in this paper can easily provide us with the input/output model of a specific sensor by doing only one experiment; it also improves the accuracy of the measurements as it is a self-calibrating technique that reduces the nonlinearity and noise problems to deliver a better estimation of the measured signal, which is validated in this paper experimentally using a low-cost current sensor by comparing the obtained results from this algorithm with the results using the extracted input/output model illustrated in the datasheet. Furthermore, if the sensor is pretty poor, and if the application requires more precision, the provided estimation by the mapping function can be mixed with other sensor/s readings using sensor fusion algorithms to find a more precise value of the input. The represented algorithm can also perform self-calibration while evaluating the functionality of the application and the variations of the temperature and other external disturbances that affect the sensor.

This paper presents an effective quasi open-loop (QOLS) synchronization technique for grid -connected power converters that is organized in two different blocks. The first block is a new flexible technique for extracting the positive and negative sequence voltage under unbalanced and distorted conditions. It is a decoupled double self-tuning filter (DD-STF) or multiple self-tuning filters (M-STF) according to the conditions. The main advantages of this technique are its simple structure and the fact of being able to work under highly distorted conditions. Each harmonic is separately treated and this allows for selective compensation in active filter applications. The second block is the frequency detector; we propose a neural approach based on an ADALINE for online adaptation of the cut-off frequency of the DD-STF and M-STF considering a possible variation in the main frequency. The main advantage of this method is its immunity to the voltage signal amplitude and phase. In order to improve the performance of the frequency estimation under distorted source voltage, a pre-filtering stage is introduced. Experimental tests validate the proposed method and illustrate all its interesting features. Results show high performance and robustness of the method under low voltage ride through.

In this paper, we propose a direct power control (DPC) using a second order sliding mode control (SOSMC) strategy and artificial neural network (ANN) for the doubly-fed induction generator (DFIG), which is applied to wind turbine system. The main objective of this intelligent technique is to replace the switching table and the hysteresis comparators with a NSOSMC control to reduce the ripple to the level of current and power. The best performances offered by the control technique proposed in the context of this work are verified by simulation using the Matlab / Simulink software. The proposed conventional direct power control (C-DPC) strategy produces a fast and robust power response. However, it suffers from the major problem which resides in the lack of control of the switching frequency which has a large variation which generates ripples at the level of the various quantities characterizing the DFIG. In this context and in order to overcome this problem, the C-DPC has been replaced by the intelligent NSOSMC control, which have a better response to variations in machine parameters.

To get the most power out of photovoltaic (PV) panels, PV systems must utilize a maximum power point tracking (MPPT) controller. In partial shading conditions (PSC), the power-voltage (P-V) characteristic of the PV network may show a single global maximum power point (GMPP) and two or more local maximum power points (LMPP). This indicates that the PV cells and panels do not get uniform illumination. As they converge on the maximum power point (MPP) that makes contact first, which is often one of the LMPPs in this scenario, common MPPT approaches like incremental conductance (InC) and perturb and observe (P&O) are unable to distinguish between a GMPP and LMPPs. In this paper, the extraction of the GMPP of the PV system under PSC based on a suggested particle swarm optimization (PSO) approaches is presented. The particularity of the suggested approach is that it takes into account the calculation of the position of each particle as a function of the duty cycle and the global maximum power. Results on the performance of the suggested PSO method show an advantage over the conventional PSO and the commonly used traditional P&O method. The suggested PSO technique offer better performance in terms of global power extracted, ripple rate of the power and efficiency. [ABSTRACT FROM AUTHOR]