This chapter addresses the estimation of the Total Harmonic Distortion (THD) of the drive input current, which is an interesting performance indicator of the variable speed drive. The analysis of the complete drive system is done (study of the dynamic model of the DC bus and its stability, frequency analysis of the input signals). A simplified estimator of the THD is proposed to be able to embed the calculation algorithm in real-time Drive control load with good accuracy and depending on the conduction mode of the DC choke (continuous or discontinuous).

In some heating, ventilating, air-conditioning, and refrigerant (HVAC-R) systems, a limitation on the temperature differential between the interior temperature setpoint and the outdoor temperature can exist to limit energy consumption. For example, when a temperature differential limit = 5 °C and the outdoor temperature = 30 °C, the effective temperature setpoint cannot be below 25 °C even if the user requires a temperature setpoint at 20 °C. This option is effective in terms of energy savings but will affect the user comfort. Air mixing technology with registers (dampers) allows for energy savings by mixing air from interior with air from outdoor (new air). This mixed air will be sent to the evaporator in the air-conditioning (AC) configuration, or to the condenser in heat pump (HP) configuration. In the case of 100% recirculated air and stabilized system, the air temperature at the evaporator outlet will not be directly impacted by outdoor temperature/humidity, contrary to the case of non-air-mixing.

Electrical motors are the most important source of mechanical energy in the industrial world. Their modeling traditionally relies on a physics-based approach, which aims at taking their complex internal dynamics into account. In this paper, we explore the feasibility of modeling the dynamics of an electrical motor by following a data-driven approach. which uses only its inputs and outputs and does not make any assumption on its internal behaviour. We propose a novel encoder-decoder architecture which benefits from recurrent skip connections. We also propose a novel loss function that takes into account the complexity of electrical motor quantities and helps in avoiding model bias. We show that the proposed architecture can achieve a good learning performance on our high-frequency high-variance datasets. Two datasets are considered: the first one is generated using a simulator based on the physics of an induction motor and the second one is recorded from an industrial electrical motor. We benchmark our solution using variants of traditional neural networks like feedforward, convolutional, and recurrent networks. We evaluate various design choices of our architecture and compare it to the baselines. We show the domain adaptation capability of our model to learn dynamics just from simulated data by testing it on the raw sensor data. We finally show the effect of signal complexity on the proposed method ability to model temporal dynamics.

This paper proposes a method based on signal injection to obtain the saturated current-flux relations of a PMSM from locked-rotor experiments. With respect to the classical method based on time integration, it has the main advantage of being completely independent of the stator resistance; moreover, it is less sensitive to voltage biases due to the power inverter, as the injected signal may be fairly large.

The goal of this paper is to propose a model of the (cross-) saturated SynRM suitable for sensorless control purposes, together with an identification procedure suitable for reasonably easy use in the field: the model contains about ten parameters, which are identified from only the drive current measurements, in experiments where the rotor is locked in a known position. The experimental procedure relies on signal injection to produce data that can be used for parameter identification.

Pumps are widely used in various domestic and industrial applications, such as in water and waste water, food and beverage, and oil and gas applications. In most cases, a pump is controlled by an asynchronous motor which converts the electrical power into the mechanical power required for the pump operation. The motor can be either connected directly to the mains (DOL) or controlled by a variable speed drive (VSD). The energy management of a global pumping system becomes very important to reduce the energy cost of the installations and optimize the maintenance cost, for instance, by increasing the lifetime of the equipment. A VSD is essential to increase the energy efficiency of the overall pump system. A VSD allows delivering all possible mechanical working points (speed, torque), and consequently, only the mechanical power required by the system. Furthermore, a VSD plays an important role in optimizing the electrical energy consumed by the motor as a function of the mechanical energy provided to the pump. This is achieved by minimizing the electrical heat energy losses. In this paper, we propose an original method for electrical energy optimization for a complete pump system including a motor and drive. The objective is to determine the optimal pump speed that minimizes the electrical energy consumption for a hydraulic operating system point. We model the system including a VSD, a motor, a pump, and the hydraulic application. Subsequently, we define the process optimization problem for a single pump and multiple pumps systems. For the single pump system, we demonstrate that the solution of the optimization problem is equivalent to minimization of the drive and motor losses. For the multiple pumps system, we show that we must also optimize the pump losses. For the hydraulic system using multiple pumps, the process demand is actually shared between these pumps. Thus, the speed of each pump is set such that the total energy consumption of the global pumping system is optimized. The simulation and experimental results exhibit the relevance of this power optimization approach for hydraulic pumping systems. (C) 2017 Elsevier Ltd. All rights reserved.

We propose a method to "create" a new measurement output by exciting the system with a high-frequency oscillation. This new "virtual" measurement may be useful to facilitate the design of a suitable control law. The approach is especially interesting when the observability from the actual output degenerates at a steady-state regime of interest. The proposed method is based on second-order averaging and is illustrated by simulations on a simple third-order system.

We propose a new sensorless position estimation method of Permanent-Magnet Synchronous Motor (PMSM) at low speed by high frequency (HF) voltage injection. This method relies on a parametric saturation model of PMSM. The rotor position is estimated from the high frequency current using averaging calculations and a magnetic saturation model. Experimental results show clearly that the rotor position can be accurately estimated for different types of PM motors such as interior magnet and surface mounted, even at large angles. The estimation error is less than five electrical degree even at low speed and high load torque.

SummaryA gradient descent‐based nonlinear observer for surface‐mount permanent magnet synchronous motors with remarkable stability properties was recently proposed. A key assumption for the derivation of the observer is the knowledge of the electrical parameters, which are usually uncertain. In the present paper, we propose a robust adaptive flux observer adding immersion and invariance parameter adaptation algorithms to estimate the stator resistance. Global boundedness of all signals and convergence to a residual set of the flux estimation error is guaranteed. The performance of the new adaptive observer is assessed with realistic simulations. Copyright © 2015 John Wiley & Sons, Ltd.

We analyze why low-speed sensorless control of the IM is intrinsically difficult, and what is gained by signal injection. The explanation relies on the control-theoretic concept of observability applied to a general model of the saturated IM. We show that the IM is not observable when the stator speed is zero in the absence of signal injection, but that observability is restored thanks to signal injection and magnetic saturation. The analysis also reveals that existing sensorless algorithms based on signal injection may perform poorly for some IMs under particular operating conditions. The approach is illustrated by simulations and experimental data.

We propose an approach to modeling of AC motors entirely based on analytical mechanics. Symmetry and connection constraints are moreover incorporated in the energy function from which the models are derived. The approach is especially suited to handle magnetic saturation, but also directly recovers the standard unsaturated models of the literature. The theory is illustrated by some experimental data.

We propose a new approach to modeling electrical machines based on energy considerations and construction symmetries of the motor. We detail the approach on the Permanent-Magnet Synchronous Motor and show that it can be extended to Synchronous Reluctance Motor and Induction Motor. Thanks to this approach we recover the usual models without any tedious computation. We also consider effects due to non-sinusoidal windings or saturation and provide experimental data.