Energy optimization of bitcoin mining integrated greenhouse with model predictive control

Due to model predictive control can better deal with the constraints problem of nonlinear systems and improve the dynamic performance of the controlled system, therefore, this technology has attracted much attention in the field of motor drive. This article first introduced the basic principle of model predictive control, continuous control set model predictive control and finite control set model predictive control. Secondly, summarized the research status of generalized predictive control, explicit model predictive control, model predictive current control, model predictive torque control and commonly used improved model predictive control in motor drive systems. Thirdly, prospected the future development trend based on the current research status of model predictive control in motor drive system. Finally, the advantages and disadvantages of the continuous control set model predictive control and the finite control set model predictive control were comprehensively compared, and the ways in which the two algorithms act on the motor drive system were summarized.

Performance and robustness evaluation of Nonlinear Autoregressive with Exogenous input Model Predictive Control in controlling industrial fermentation process

ABSTRACTTraditional model predictive control (MPC) strategy is highly dependent on the model and has poor robustness. To solve the problems, this paper proposes a robust model predictive current control strategy based on a disturbance observer. According to the current predictive model of three-phase voltage source PWM rectifiers (VSR), voltage vectors were selected by minimizing current errors in a fixed time interval. The operating procedure of the MPC scheme and the cause of errors were analysed when errors existed in the model. A disturbance observer was employed to eliminate the disturbance generated by model parameters mismatch via feed-forward compensation, which strengthened the robustness of the control system. To solve the problem caused by filter delay in MPC control, an improved compensation algorithm for the observer was presented. Simulation and experimental results indicate that the proposed robust model predictive current control scheme presents a better dynamic response and has stronger robustness compared with the traditional MPC.

Proportional-integral (PI) control and model predictive control (MPC) are mainly utilized in battery and supercapacitor (SC) hybrid energy storage system (HESS) of dc microgrid. Unfortunately, the regulation time of the PI controller is long, while large current ripples are introduced by MPC when the system frequency is low. This article proposes a new model predictive current control (new MPCC) strategy for HESS based on MPC and PI controllers. This method reduces current ripples, and meanwhile, enhances system stability and rapidity. MPC is adopted as the outer loop and it calculates reference values for the inner PI current loop. Furthermore, the state of charge (SOC) of the SC is considered based on fuzzy control to optimize the reference power of the SC inner controller. First, the topology and mathematical model of HESS in the dc microgrid are analyzed. Then based on PI control and MPC, the new MPCC is proposed, and compared with MPC and PI methods, respectively. Finally, the fuzzy control method takes SOC of SC into account to optimize the SC reference current. Simulation and experimental results show that the proposed new control strategy could effectively decrease current ripples and dynamic processes and entirely perform the high energy density characteristic of batteries and high power density characteristic of SC.

Model Predictive Control (MPC) offers a variety of advantages against linear control approaches (e.g. PI-Controller). Due to new approaches real-time implementation of MPC is already possible for permanent magnet synchronous motors with interior magnets (IPMSM). While the choice of the structure of the objective function to be minimized is quite intuitive, the adjustment of its weighting factors is not. In this paper a Model Predictive Direct Torque Control (MPDTC) approach is presented that automatically determines the weighting factors of the objective function. This self-optimizing MPDTC also offers real-time capablility for online MPC even with process time constants in the millisecond range. The good control dynamics (short settling time, low overshoot and small current ripple) as well as the differences between this self-optimizing MPC compared to the normal MPC is shown by simulation results.

This article discusses a system identification based on a black-box state-space model for an experimental electro-hydraulic servo system. Furthermore, it presents force-tracking control for the electro-hydraulic servo system based on model predictive control. The parameters of model predictive controls have been tuned by cuckoo search algorithm as well as genetic algorithm. The realization of model predictive controls depends on using a data acquisition card (NI-6014) and Simulink/MATLAB as the core of the electro-hydraulic servo system control system. In this research, the combination of model predictive control tuned by cuckoo search algorithm and genetic algorithm has been introduced in the form of switching model predictive controls. This combination collects the advantages of two model predictive controls in one model predictive control by switching model predictive controls. The simulation and experimental results display that the suggested switching of model predictive controls introduces a good tracking performance in terms of settling time, rise time, and system overshoots as compared to the two separated model predictive controls. In addition, the experimental evaluation has shown that the proposed switching model predictive controls achieved a stable and robust control system even facing to a different reference command signals (step, multistep, and sinusoidal signals). Moreover, its behavior is more robust for system parameters perturbation and small or large perturbation of disturbances in the working environment. It also achieves the necessitated physical limits of the actuator. As a general conclusion and a deep study of electro-hydraulic servo system, one can conclude that the switching strategy between model predictive control tuned by cuckoo search algorithm and by genetic algorithm has the priority of applying it on the field of electro-hydraulic servo system. The proposed new strategy (switching of model predictive control) is promising in experimental applications.

As a crucial player in medium-voltage (MV) applications, high power current-source converters (CSCs) feature some distinct advantages in contrast to their voltage-source counterparts. However, the traditional control techniques, based on linear proportional-integral (PI) regulators and low band-width modulation, impose several technical issues during low switching frequency operation. In order to meet more and more stringent performance requirements on industrial drives, various high performance finite control-set model predictive control (FCS-MPC) schemes are proposed in this thesis to control CSCs employed in MV induction motor (IM) drives. The continuous-time and discrete-time dynamic models of high power CSC-fed MV IM drive are deduced, which are used to predict the evolution of state variables in the system. Issues related to MPC approach, such as prediction horizon, weighting factor selection, control delay compensation, accurate extrapolation of references, and nature of variable switching frequency are addressed as well. Model predictive power factor control (MPPFC) is proposed to accurately regulate the line power factor of CSR under various operating conditions. Meanwhile, an active damping function is incorporated into MPPFC to suppress the possible line-side LC resonance. Moreover, an online capacitance estimation method is designed in consideration on the perturbation of the filter parameters of CSR. In order to keep fixed switching frequency of CSC and improve its dynamic responses, model predictive switching pattern control (MPSPC) and model predictive space vector pattern control (MPSVPC) are proposed, in which MPC technique is combined with selective harmonic elimination (SHE) modulation and space vector modulation (SVM), respectively. In steady state, the PWM waveform of CSC follows the pattern of traditional modulation schemes, whereas during transients CSC is governed by MPC approach for the purpose on dynamic performance improvement. A common-mode voltage (CMV) reduced model predictive control (RCMV-MPC) is studied, with which the peak value of CMV in high power CSC-fed MV IM drive can be further reduced in comparison with the traditional RCMV modulation schemes. The dynamic responses of the motor drive system are further improved as well. The simulation on a megawatt motor drive system and experimental results on a low power prototype, validate the effectiveness of the proposed various control schemes.

Yaw control system plays an important role in helping large-scale horizontal wind turbines capture the wind energy. To track the stochastic and fast-changing wind direction, the nacelle is rotated by the yaw control system. Therein, a difficulty consists in the variation speed of the wind direction much faster than the rotation speed of the nacelle. To deal with this difficulty, model predictive control has been recently proposed in the literature, in which the previewed wind direction is employed into the predictive model, and the estimated captured energy and yaw actuator usage are two contradictive objectives. Since the performance of the model predictive control strategy relies largely on the weighting factor that is designed to balance the two objectives, the weighting factor should be carefully selected. In this study, a fuzzy-deduced scheme is proposed to derive the weighting factor of the model predictive yaw control. For the proposed fuzzy-deduced strategy, the variation degree and the increment of the wind direction during the predictive horizon are used as the inputs, and the weighting factor is the output, which is dynamically adjusted. The proposed model predictive yaw control is demonstrated by some simulations using real wind data and its performance is compared with the conventional model predictive control with the fixed weighting factor. Comparison results confirm the outweighing performance of the proposed control strategy over the conventional one.

As a crucial player in medium-voltage (MV) applications, high power current-source converters (CSCs) feature some distinct advantages in contrast to their voltage-source counterparts. However, the traditional control techniques, based on linear proportional-integral (PI) regulators and low band-width modulation, impose several technical issues during low switching frequency operation. In order to meet more and more stringent performance requirements on industrial drives, various high performance finite control-set model predictive control (FCS-MPC) schemes are proposed in this thesis to control CSCs employed in MV induction motor (IM) drives. The continuous-time and discrete-time dynamic models of high power CSC-fed MV IM drive are deduced, which are used to predict the evolution of state variables in the system. Issues related to MPC approach, such as prediction horizon, weighting factor selection, control delay compensation, accurate extrapolation of references, and nature of variable switching frequency are addressed as well. Model predictive power factor control (MPPFC) is proposed to accurately regulate the line power factor of CSR under various operating conditions. Meanwhile, an active damping function is incorporated into MPPFC to suppress the possible line-side LC resonance. Moreover, an online capacitance estimation method is designed in consideration on the perturbation of the filter parameters of CSR. In order to keep fixed switching frequency of CSC and improve its dynamic responses, model predictive switching pattern control (MPSPC) and model predictive space vector pattern control (MPSVPC) are proposed, in which MPC technique is combined with selective harmonic elimination (SHE) modulation and space vector modulation (SVM), respectively. In steady state, the PWM waveform of CSC follows the pattern of traditional modulation schemes, whereas during transients CSC is governed by MPC approach for the purpose on dynamic performance improvement. A common-mode voltage (CMV) reduced model predictive control (RCMV-MPC) is studied, with which the peak value of CMV in high power CSC-fed MV IM drive can be further reduced in comparison with the traditional RCMV modulation schemes. The dynamic responses of the motor drive system are further improved as well. The simulation on a megawatt motor drive system and experimental results on a low power prototype, validate the effectiveness of the proposed various control schemes.

As a crucial player in medium-voltage (MV) applications, high power current-source converters (CSCs) feature some distinct advantages in contrast to their voltage-source counterparts. However, the traditional control techniques, based on linear proportional-integral (PI) regulators and low band-width modulation, impose several technical issues during low switching frequency operation. In order to meet more and more stringent performance requirements on industrial drives, various high performance finite control-set model predictive control (FCS-MPC) schemes are proposed in this thesis to control CSCs employed in MV induction motor (IM) drives. The continuous-time and discrete-time dynamic models of high power CSC-fed MV IM drive are deduced, which are used to predict the evolution of state variables in the system. Issues related to MPC approach, such as prediction horizon, weighting factor selection, control delay compensation, accurate extrapolation of references, and nature of variable switching frequency are addressed as well. Model predictive power factor control (MPPFC) is proposed to accurately regulate the line power factor of CSR under various operating conditions. Meanwhile, an active damping function is incorporated into MPPFC to suppress the possible line-side LC resonance. Moreover, an online capacitance estimation method is designed in consideration on the perturbation of the filter parameters of CSR. In order to keep fixed switching frequency of CSC and improve its dynamic responses, model predictive switching pattern control (MPSPC) and model predictive space vector pattern control (MPSVPC) are proposed, in which MPC technique is combined with selective harmonic elimination (SHE) modulation and space vector modulation (SVM), respectively. In steady state, the PWM waveform of CSC follows the pattern of traditional modulation schemes, whereas during transients CSC is governed by MPC approach for the purpose on dynamic performance improvement. A common-mode voltage (CMV) reduced model predictive control (RCMV-MPC) is studied, with which the peak value of CMV in high power CSC-fed MV IM drive can be further reduced in comparison with the traditional RCMV modulation schemes. The dynamic responses of the motor drive system are further improved as well. The simulation on a megawatt motor drive system and experimental results on a low power prototype, validate the effectiveness of the proposed various control schemes.

Comparison and analysis of predictive control of induction motor without weighting factors

Model predictive control (MPC) represents an affirmed optimal control strategy, able to handle multivariable systems and their input-output constraints. However, MPC does not provide an integral control action for reference tracking control problems. Several methods have been proposed to overcome this limitation. Standard MPC methods include a disturbance observer to handle unmodeled uncertainties, such as external unknown disturbances and parameter mismatches. Among these formulations, the authors focus on the velocity form MPC, which considers the incremental formulation of the motor state-space model. This formulation gets rid of the bias errors in reference tracking problems. In this article, the MPC paradigm is applied to the current control of synchronous motor drives. The intent is to compare the velocity form and the MPC with disturbance observer. A theoretical analysis of the MPC coupled with disturbance observers and the equivalence between these formulations and the velocity form is presented. Input constraints are included in the MPC optimization process, thus requiring an online quadratic programming solver. Experimental tests consider a 1 kW anisotropic synchronous motor. Numerical aspects regarding the optimization problem are investigated for both methods.

Modern multi-megawatt wind turbines require powerful control algorithms which consider several control objectives at the same time and respect process constraints. Model predictive control (MPC) is a promising control method and has been a research topic for years. So far, very few studies evaluated MPC algorithms in field tests. This work aims to prepare a real-time MPC system for a full-scale field test in a 3 MW wind turbine. To this end, we introduce a weight-scheduling scheme for a linear time-variant MPC in order to ensure control operation over the entire operating range from the partial to the full load range. We use a rapid control prototyping process, in particular with comprehensive software-in-the-loop (SiL) tests, in order to design and validate the MPC system for the field test.In this contribution, we present the implementation of the linear time-variant MPC with weight-scheduling to be tested in the field test. With the weight-scheduling for the optimization problem inside the MPC, we achieved good performance over the entire operating range of the wind turbine. In the SiL tests, the proposed MPC algorithm achieved loads, comparable to the baseline controller of the wind turbine and improved the reference tracking of the power output and the rotational speed. The proposed linear time-variant MPC with weight-scheduling is fully validated in the presented software-in-the-loop tests and is ready for full-scale field test in the 3 MW wind turbine. We present the experimental field test results of the introduced MPC system in a separated contribution.

This article presents a 125-day experiment to investigate model predictive heat pump control. The experiment was performed in two parallel operated systems with identical components during the heating season. One of the systems was operated by a standard controller and thus represented a reference to evaluate the model predictive control. Both test rigs were heated by an air-source heat pump which is influenced by real weather conditions. A single-family house model depending on weather measurement data ensured a realistic heat consumption in the test rigs. The adapted model predictive control algorithm aimed to minimize the operational costs of the heat pump. The evaluation of the measurement results showed that the electrical energy demand of the heat pump can be reduced and the coefficient of performance can be increased by applying the model predictive controller. Furthermore, the self-consumption of photovoltaic electricity, which is calculated by means of a photovoltaic model and global radiation measurement data, was more than doubled. Consequently, the energy costs of heat pump operation were reduced by 9.0% in comparison to the reference and assuming German energy prices. The results were further compared to the scientific literature and short-term measurements were performed with the same experimental setup. The dependence of the measurement results on the weather conditions and the weather forecasting quality are shown. It was found that the duration of experiments should be as long as possible for a comprehensive evaluation of the model predictive control potential.

As advanced control strategies, both iterative learning control (ILC) and model predictive control (MPC) are widely used in industrial process. Because ILC cannot eliminate the non-repetitive disturbances, ILC and MPC are integrated as model predictive iterative learning control (MPILC) to improve the capability of rejecting disturbances. Although the typical MPILC has a good tracking performance, there is also left some aspects to be developed. Based on a fuzzy model, a modified nonlinear model predictive iterative learning control (NMPILC) is proposed to achieve a better tracking performance and speed up the learning rate. The performance of the modified NMPILC is illustrated by a PH neutralization process.