Currently, the most implemented solution for driving a pumping system (PS) at variable speed is using a variable frequency drive (VFD). Because most of the VFDs have integrated only one proportional-integral (PI) frequency controller whose parameters (proportional gain/the integration time) can be off-line tuned but cannot be changed during real-time operation, and many PS must operate in different regimes (at variable speed, variable flow rate, at variable pumping head or even at variable power, e.g., those powered by renewable energy sources), the adaptive control strategy proposed in this paper overcomes these problems with very good performances. The proposed adaptive control strategy uses only simple PI controllers for managing several operating regimes. The adaptive character is not ensured by re-tuning the PI controller parameters as in self-tuning controllers but by readjusting the control law through a change in the control loop depending on the controlled output of the process (pump speed, pump discharge, pumping head or absorbed power). The deviations of the mentioned controlled outputs from their referenced values are converted into electrical frequency error (the deviation of the current value from its reference), which is the regular input to the already tuned controller; therefore, no controller re-tuning is required.

The paper proposes Markov models for the reliability analysis of the wireless sensor networks. There are presented the theoretical aspects and some of the variables that are used in the domain of fault tolerant systems. There is also presented a comparison between systems using dedicated replacements and universal replacements for defective nodes. There has been conducted a study regarding the reliability of system versus cost price, considering different number of hot redundant replacement parts.

In recent years, many pumping systems have begun to be powered by renewable energy generators, including mostly photovoltaic generators and, less frequently, electrical wind generators. Because of the technology’s complexity and novelty (it has not yet reached its maturation), most of those systems consist of single pumps powered by photovoltaic generators or electrical wind generators. For this reason, the current paper proposes a strategy for driving a multi-pump system with water storage powered by a fluctuating and intermittent power source, such as power grids, which are limited by price variation over different periods during the day, or photovoltaic generators and/or electrical wind generators. The current work begins by proposing a model of a multi-pump system with water storage, followed by the design of a control strategy for operating such a system powered by a fluctuating and intermittent power source in an energy-efficient manner, without sacrificing the reliability, robustness and lifetime of the plant. Finally, an analysis of two concrete situations encountered in practice is made: in one, the considered multi-pump system is powered only by a power grid limited by price variation over three periods; in the other, it is powered by a photovoltaic generator.

This paper presents the implementation of a neural network-based fault detection for wireless sensor networks (WSNs). There are discussed two proposed approaches: both a centralized and respectively distributed fault detection. The methods are validated using a wireless sensor network that takes environmental measurements every 60 seconds. There were considered various fault scenarios that were detected and corrected by the implemented structure. Neural networks (NNs) are used to model the environmental parameter's dynamics. The physical measurement is compared against the predicted value and a given threshold of error to determine sensor fault.

The present paper presents specific issues that arise in Photovoltaic Energy Conversion Systems, being focused on maximizing the output power. There is proposed a model for Photovoltaic PV panel external characteristics. The coordinates of maximum power point are determined and used for imposing the appropriate load resistance to maximize the provided power. A comparison is made considering the classical control methods and the proposed optimized method, that prove better performances than methods based on output power measurement.