Associate Professor Galina Mirzaeva

Model predictive control (MPC) has been widely advocated as a design strategy for many aspects of industrial electronics. The methodology has been strongly promoted by some researchers but has also attracted criticism from others. In this context, the purpose of this paper is twofold. First, we show that many existing and popular control strategies, including finite set MPC and linear controllers [proportional integral, proportional resonant (PR)], can be viewed as special cases of MPC. Second, we show that the predictive control framework allows one to embellish these classical control architectures with novel features and to design new and advanced control architectures to address various challenges posed by power electronics applications. The findings of the paper are supported by a practical example of designing of a novel form of PR controller with superior tracking performance and delay compensation, confirmed via simulation and experiments.

© 2014 Taylor and Francis. Inverters play a central role in modern society including renewable energy integration and motor drives. Due to the inherent switched nature of the inverter waveforms harmonic distortion is an issue. Additionally, the switching patterns are perturbed by unavoidable switching delays. Amongst those, nonlinear and load-dependent switching delays (known as inverter dead-time delays) are the most difficult to compensate. In this paper, we propose a new approach to delay compensation and harmonic suppression in inverter voltage. The proposed approach is based on variable prediction horizon nonlinear model predictive control.

Matrix Converters (MC) have prominent advantages in AC/AC power conversion applications, for example, in aerospace and surface transport. However, due to a tight interconnection between its input and output, it is difficult to achieve satisfactory performance on both sides, simultaneously. This paper proposes a novel, optimisation based, design approach for the modulation (duty cycle) matrix associated with the MC. Under this approach, the entries of the modulation matrix are used as decision variables in a quadratic optimisation programme. The optimisation combines control requirements both at the input and output sides of the converter in a non-conflicting manner. The paper includes simulation results, which demonstrate that the proposed solution complies with the objectives of both sides. Thus high quality tracking at the MC output is achieved simultaneously with input resonance suppression and power factor control at the MC input. An estimate of the computational load is provided which suggests that a real-time implementation is easily achievable.

This paper proposes a new method for measurements-based modeling of nonlinear loads in power systems. This method includes a combination of a binary tree algorithm with Nonlinear Auto-Regressive with Exogenous Input (NARX) identification. The paper demonstrates that the new method performs well without any prior knowledge of the system structure. In contrast to other load modeling methods, which are typically aimed for particular studies or load types, the proposed method can be used with any load type and for any study. Accurate load modeling is particularly important for studies of industrial networks and grids. In this study, a field data set was collected in a mine site from a large electrical rope shovel. This data set has been used to develop a model of the rope shovel based on the proposed binary tree - NARX algorithm. When compared to other known methods, such as Wavelet and Sigmoid networks, the proposed method has shown the fastest training time and the highest accuracy. Finally, the modeling results have been verified against another set of field measurements from an existing network, and have shown a very good agreement.

This paper proposes a new interpretation of Finite Set Model Predictive Control (FS-MPC) for inverters. This interpretation gives insights into the existing limitations of FS-MPC and suggests mitigation strategies. Consequently, significant improvements of FS-MPC performance are achieved, including elimination of steady state error, improved harmonic spectrum, reduced control delays, etc. The proposed new MPC interpretation also establishes relationships between various control schemes, including FS-MPC and PWM-MPC, FS-MPC and MPC with integral action, etc. Additionally, the paper proposes further embellishments to FS-MPC based on its more efficient and accurate implementation. The findings of the paper are supported by simulations and experimental results.

Model Predictive Control (MPC) has received significant attention from the power electronics research community. In this paper we apply MPC approach to designing optimal current regulators for voltage source inverters (VSI), in the sense of tracking references and rejecting disturbances of interest. In particular, we present a detailed derivation of optimal current regulators for tracking references and rejecting disturbances of constant and harmonic types. We compare these to the existing control structures, such as proportional integral (PI) and proportional resonant (PR) current regulators. We further propose a strategy to improve their performance based on matching their structure and parameters to the optimal controllers. The findings of the paper are supported by PSIM simulations and preliminary experimental results.

Recently, DC microgrids have become a viable option for mining distribution systems. Fault diagnostics and protection are known challenges in DC microgrids. In this paper, we present an overview of the existing strategies for fault diagnostics and protection of a DC microgrid and apply them to an open cut DC mine scenario, which includes large electric excavators. We propose a comprehensive fault tolerance strategy based on analysis of electrical and mechanical operations of an electric rope shovel and identification of its working cycle. Based on this strategy, in case of a fault, the electric rope shovel will be able to perform required actions to avoid collisions and safely finish its working cycle, while maintaining electric safety.

Model Predictive Control (MPC) has attracted significant attention from the power electronics community. In this body of work it is usual to utilize a linear load model. Also, to deal with computational complexity, it is common to deploy horizon 1 optimization. A major difficulty with these standard approaches is that modelling errors, e.g. those arising from non-linear effects such as magnetic saturation, lead to poor tracking performance. In this paper we show that perfect tracking is achievable provided that MPC controller is augmented by a suitable observer.

A new fault diagnosis scheme based on the monitoring of main air gap flux of the three phase squirrel cage induction motor is proposed. Most of the existing electrical techniques of condition monitoring are based on the stator current and/or leakage flux measurement outside the motor. Besides, most of these methods only concentrate on the time harmonics analysis of the acquired signal. We propose to analyse the space harmonics of the main air gap electromagnetic flux along with the time domain analysis. To this aim, we propose to monitor the main air gap flux using Hall Effect Flux Sensors (HEFS) at all the stator slots of an induction motor, which can be used to address the stator and rotor slot effects not only through frequency analysis of the time varying magnetic flux, but also by magnitude and phase shift comparison of sensors located at different geometric positions around the stator. Moreover, by using HEFS in all the stator slots, the flux density along the circumference of the stator can be acquired and the space harmonics of the flux can be analysed, which shown promising results in detecting and locating different faults. In this paper, we concentrate on detecting different types of rotor faults such as broken rotor bar fault and eccentricity.

A new fault diagnosis scheme based on the monitoring of main air gap flux of squirrel cage induction motors is proposed. Most of the existing flux monitoring techniques are based on the leakage or stray flux measurement outside of the motor. A few methods, however, use the main air gap flux as the fault signature, where search coils are used to monitor the derivative of the flux, which eventually introduces noise in the signal. Moreover, the diagnosis methods are mainly based on detecting a fault, whereas very little initiative has been taken to locate a fault precisely. To address these problems, a sophisticated yet robust condition monitoring and fault diagnosis method is needed. To this aim, we propose to monitor the main air gap flux using Hall Effect Flux Sensors (HEFS) at all the stator slots of an induction motor, which can be used to address the stator and rotor slot effects not only through frequency analysis of the magnetic flux, but also by magnitude and phase shift comparison of sensors located at different geometric positions around the stator. We have successfully detected the stator turn-to-turn fault at a very incipient stage and detected the location of the fault precisely. Promising results have been obtained through simulation in the case of broken rotor bar faults as well.

Condition monitoring of an induction motor may be based on measuring the electrical signals, such as stator current, electromagnetic flux etc. or mechanical signals such as vibration, noise etc. from the motor. Most of the existing electrical techniques of condition monitoring and fault diagnostics are based on the stator current and/or leakage flux measurement outside the motor. Besides, most of these methods only concentrate on the time harmonics analysis of the acquired signal. But the main air gap flux consists of both time and space harmonics. The latter is overlooked by most of the existing researches. We propose to analyse the space harmonics of the main air gap electromagnetic flux along with the time domain analysis. But to do so, a thorough knowledge of the space harmonics of the stator flux, the rotor flux and their insights is needed to be obtained. To this aim, we present the space harmonics analysis of the main air gap flux of a healthy induction in this paper. Then the applicability of the space harmonics analysis to detect the faults is discussed. Two types of rotor faults, i.e. eccentricity and the broken rotor bar fault are discussed.