Gionata Cimini

Supervisory control of PV-battery systems by online tuned neural networks

The paper deals with a neural network based supervisor control system for a PhotoVoltaic (PV) plant. The aim of the work is to feed the power line with the 24 hours ahead forecast of the PV production. An on-line self-learning prediction algorithm is used to forecast the power production of the PV plant. The learning algorithm is based on a Radial Basis Function (RBF) network and combines the growing criterion and the pruning strategy of the minimal resource allocating network technique. The power feeding the electric line is scheduled by a Fuzzy Logic Supervisor (FLS) which controls the charge and discharge of a battery used as an energy buffer. The proposed solution has been experimentally tested on a 14 KWp PV plant and a lithium battery pack.

Control of variable speed wind energy conversion systems by a discrete-time sliding mode approach

This paper presents a discrete-time variable-structure-based control designed for a variable speed wind energy conversion system based on a permanent magnet synchronous generator. A cascade control scheme is proposed to obtain the maximum power efficiency. The analysis of the control stability is given and the ultimate boundedness of the speed tracking error is proved. Reported numerical simulations show that the proposed controller enhances the overall performance obtaining the maximum power efficiency and it is robust under variable operating conditions and in the presence of disturbances affecting the system.

Online model predictive torque control for Permanent Magnet Synchronous Motors

In this paper a Model Predictive Control (MPC) method for torque control of a Permanent Magnet Synchronous Motor (PMSM) is presented. The proposed approach takes into account constraints on voltages and currents, and allows the use of modulation techniques that eliminate the side effects caused by the direct transistor actuation performed by Model Predictive Direct Torque Control (MP-DTC) approaches. The optimization problem resulting from the proposed MPC formulation is solved online, in contrast with what is done in explicit MPC, where the optimal control law is obtained offline by multiparametric optimization. The performance of the proposed control strategy is evaluated in Processor-In-the-Loop (PIL) experiments, carried out on a low cost Digital Signal Processor (DSP) commonly used in motion control. Results show that the proposed approach is able to improve the torque dynamics with respect to a conventional controller, and that an embedded implementation is feasible in terms of required computational power and memory.

Passivity-Based PFC for Interleaved Boost Converter of PMSM Drives

Abstract In this paper a sensorless passivity-based control with input voltage feedforward has been designed for a boost AC-DC interleaved converter to increase the Power Factor (PF) in Permanent Magnet Synchronous Motor (PMSM) drives. A robust current observer has been adopted. The proposed solution has been experimentally tested on a commercial PMSM drive equipped with a control system based on a microcontroller (MCU) of the Texas Instrument (TI) family C 2000 ™.

Current sensorless solution for PFC boost converter operating both in DCM and CCM

In this paper a current sensorless solution for Power Factor Control (PFC) of an AC-DC boost converter operating in light load condition has been presented. A PI-based control with input voltage feedforward has been implemented in order to increase Power Factor (PF). A current observer, able to operate in either Continuous Conduction Mode (CCM) and in Discontinuous Conduction Mode (DCM), has been adopted in order to eliminate the need for expensive current sensors and to improve boost converter performances under light load conditions. The proposed solution has been numerically tested in three different load conditions using a powerful software simulation platform.

Indoor thermal comfort control through fuzzy logic PMV optimization

Control and monitoring of indoor thermal conditions represent crucial tasks for peoples satisfaction in working and living spaces. Among all standards released, predicted mean vote (PMV) is the international index adopted to define users thermal comfort conditions in thermal moderate environments. PMV is a nonlinear function of various quantities, which generally limits its applicability to the heating, ventilation, and air conditioning (HVAC) control problem. Furthermore this index does not consider explicitly outdoor weather conditions. In order to overcome both problems, we introduce a novel fuzzy controller for HVAC systems. The control, considering PMV index value as well as outdoor weather conditions, has been experimentally tested in a working space in the central east coast of Italy. Furthermore temperature regulation performances have been compared with those of a classical PID.

A Smart Lighting System for Visual Comfort and Energy Savings in Industrial and Domestic Use

Abstract The goal of this work is to develop a smart light-emitting diode lighting system for industrial and domestic use with several advantages over conventional systems, namely energy saving, high reliability, and visual comfort of interior lighting. This is achieved by integrating a smart control module and a fault diagnosis and prognosis module within a conventional lighting system. The first module controls the lighting level in an energy-efficient way, keeping a desired light level where it is needed while regulating it to a minimum where not required; this is achieved by fully exploiting fuzzy logic and proportional-integrative-derivative controllers. The second module performs fault diagnosis on the light-emitting diode system and predicts when light-emitting diode maintenance should be performed; this is achieved by employing both hardware redundancy and signal-based approaches. Interaction between the two modules permits maintenance of a desired level of light even in the case of failures on one or more light-emitting diodes. The overall system has been experimentally validated in three different scenarios.

Explicit sensorless model predictive control of synchronous buck converter

In this paper a Model Predictive Control (MPC) for buck synchronous DC-DC converter has been designed. The proposed solution considers an off-line explicit MPC formulation resulting in a PieceWise Affine (PWA) controller structure. A model based observer for inductor current has been taken into account to overcome the several problems related to sensors in high current ripple DC converters. Reported numerical results show that the proposed control scheme enhances the overall performances of common averaged current mode control with linear regulators.

Model predictive control solution for Permanent Magnet Synchronous Motors

In this paper a speed and current control for Permanent Magnet Synchronous Motors (PMSMs) with incremental encoder sensor has been designed. A Model Predictive Control (MPC) technique for PMSM has been explored, considering an off-line explicit MPC solution resulting in a PieceWise Affine (PWA) controller formulation. Reported numerical results show that the proposed controller enhances the overall performances of common cascaded Field Oriented Control (FOC) in different scenarios. The proposed solution has been experimentally tested on a commercial PMSM equipped with a control system based on Digital Signal Processor (DSP).

Model predictive control for the reference regulation of current mode controlled DC-DC converters

The control of DC-DC converters for high performance applications usually relies on Current Mode Control (CMC). Compared to voltage mode control, it guarantees automatic over-current protection, and a better closed loop stability, together with improved transient response. This paper presents a Model Predictive Control (MPC) algorithm for the regulation of the voltage reference signal in CMC. The control of pre-compensated systems via MPC is common in several fields, such as automotive and aerospace, and power electronics is a perfect candidate to exploit this hierarchical structure as well. Indeed, controllers for power converters are often coded in the integrated circuits, and cannot be changed. Furthermore the possible multirate structure allows to exploit the performance of MPC less affecting the computational cost. The paper describes the design of an MPC regulator for a synchronous buck converter, when a primal CMC is already coded. Performance improvements of the proposed controller are reported.