In this paper, the drawbacks of the conventional f-t frame based maximum torque per ampere (MTPA) control schemes are analyzed and mathematically proved. In order to inherit the merits of both the direct flux vector control (DFVC) in field weakening region and field orientated control (FOC) in constant torque region while avoiding their disadvantages, an integrated control scheme is proposed. The proposed control scheme integrates the FOC into f-t reference frame at low speeds to achieve a relatively accurate and robust MTPA control, while at high speeds, the DFVC is adopted to utilize the advantages of f-t frame based control scheme in field weakening region. A shape function is utilized by the proposed control scheme to achieve a smooth transition between the two control schemes. The proposed control scheme is verified by experiments under various operation conditions on a prototype IPMSM drive. The simulation and experimental results illustrate that the proposed control scheme could achieve a better MTPA control accuracy in constant torque region and a better field weakening performance in the constant power region. Meanwhile the complex look-up tables for FOC in field weakening region and the difficulties in observing flux vector at low speed are avoided.

/pdf/integration-of-foc-with-dfvc-for-interior-permanent-magnet-3u8pfumlxp.pdf

Integration of FOC With DFVC for Interior Permanent Magnet Synchronous Machine Drives

This article proposed a novel optimal robust control design approach to solve the uncertain parameter problem in the permanent magnet synchronous motor (PMSM) system by integrating the fuzzy set theory and system control theory First, we adopt the prescribed fuzzy number to describe the time-varying but bounded uncertain parameter in the PMSM system Second, based on the estimated bound of the uncertain parameter and the nominal dynamical model, we design a deterministic robust control scheme characterized by model-based control and feedback control The control objective is to guarantee the PMSM system with uncertain parameters satisfies the desired motion equation with deterministic system performance: uniform boundedness (UB) and uniform ultimate boundedness (UUB) Third, using the fuzzy uncertain parameter, a performance index relevant to the steady-state performance and control cost is constructed A parameters design optimization problem is formulated by minimizing the performance index, whose analytical solution is successfully solved and proved to always exists and is unique As a result, the resulting PMSM system under the proposed control approach has two-level system performance: the guaranteed deterministic performance and optimal performance Finally, the results of numerical simulation and experiment are presented for demonstration

/pdf/designing-robust-control-for-permanent-magnet-synchronous-3buftb1med.pdf

Designing Robust Control for Permanent Magnet Synchronous Motor: Fuzzy Based and Multivariable Optimization Approach

The Permanent Magnet Synchronous Motor (PMSM) is widely used in many fields. Aiming at nonlinearity, strong coupling and uncertainty of the PMSM, this paper proposes a nonlinear multi-input multi-o...

https://journals.sagepub.com/doi/pdf/10.1177/1687814020984255

The decoupled vector-control of PMSM based on nonlinear multi-input multi-output decoupling ADRC:

When traditional proportional integral and differential controllers are applied to speed control in permanent magnet synchronous motors(PMSM), their coefficients are basically determined based on experience, which inevitably leads to unsatisfactory results when using this parameter to control the speed stability of permanent magnet synchronous motors. Therefore, this paper proposes an improved quantum genetic algorithm using quantum states as the basic unit. Utilizing quantum properties for global optimization to optimize the coefficients of proportional integral and differential control, improving the rotation angle of quantum state particles through the idea of velocity changes in particle swarm optimization(PSO), introducing adaptive weight changes, using Hadamard gates to replace traditional algorithm mutation strategies, and incorporating disaster mechanisms. In addition, this paper uses four test functions to find the minimum value, thereby verifying that our algorithm has better performance in optimization iteration compared to other algorithms, providing the initial basis for the next step of application in PID parameter optimization. Prove that this method can solve the problem of traditional genetic algorithms falling into local optima due to improper selection, crossover, and mutation methods, which cannot effectively control the stability of motor speed. Finally, this paper uses Matlab2018a simulation to compare with the other four algorithms, and the results show that this algorithm can find better PID parameter values to achieve better results in motor oscillation, overshoot, and faster target speed. 

/pdf/pid-controller-for-pmsm-speed-control-based-on-improved-8mxzwo5b.pdf

PID Controller for PMSM Speed Control Based on Improved Quantum Genetic Algorithm Optimization

Phase-locked loops (PLLs) are widely popular in applications of grid synchronization for single- and three-phase utility power systems. However, these PLL-based methods are usually designed for constant frequency grids and these schemes only have fast dynamics and good accuracy in a small local region (i.e., phase-locked region). When applying these PLLs in the grids of more electric aircraft (MEA), in which the grid frequency varies in a wide range (360–800 Hz), both the dynamic response and the steady-state accuracy of PLLs reduce significantly. In light of this, this article presents a global convergence method to estimate the frequency, positive- and negative-sequence components of grid voltage in MEA power systems. By analyzing the relationship between the DC-offset component, the grid voltage, and their derivatives, a mathematic model with the unknown parameters, which are the DC-offset component and the grid frequency, is established. Accordingly, a global estimator is proposed to estimate the grid voltage parameters accurately. A Lyaponov-based argument ensures that the proposed estimator is global convergent and it has the capability to eliminate the DC offset effect completely. The experimental results are provided to verify the effectiveness of the proposed estimator.

A Global Convergence Estimator of Grid Voltage Parameters for More Electric Aircraft

In this paper, a novel direct torque control (NDTC) scheme utilizing composite active vectors based on discrete duty ratio modulation method is presented for permanent magnet synchronous motor (PMSM) in industrial robot servo control system. The errors in the PMSM driven by conventional DTC (CDTC) are compensated by single active vector during each control period, leading to unsatisfied steady-state performance. To improve the operation performance of the industrial robot servo control system effectively, the errors in the PMSM are compensated independently in NDTC. Therefore, two active vectors should be applied to compensate flux error and torque error, respectively, namely flux active vector and torque active vector. In order to maintain the merit of the fast dynamic response in CDTC, the introduced active vectors should be selected from the fixed six active vectors supplied by a two-level voltage source inverter. To describe the scheme of NDTC clearly, the angle between the applied active vector and the stator flux linkage is used to represent the error compensational effect provided by the applied active vector, namely active angle. Additionally, the compensational effect is defined as active factor. Subsequently, the duty ratio value of the active vector applied time can be derived easily. To simplify the calculations of the duty ratio values, novel discrete calculation method for the active factor by hysteresis controllers is presented in this paper. The effectiveness of the proposed NDTC strategy is verified through the experimental results on a 100-W PMSM drive system.

/pdf/high-precision-servo-control-of-industrial-robot-driven-by-4fzefke20p.pdf

High-Precision Servo Control of Industrial Robot Driven by PMSM-DTC Utilizing Composite Active Vectors

A Hybrid Filtering Stage Based Quasi-type-1 PLL under Distorted Grid Conditions

A novel control strategy that is based on iterative calculation of structural parameters is proposed for grid-connected inverter in this paper. The proposed strategy has a good dynamic performance, which makes it particularly suitable for the application of PV grid-connected generation. First, a second-order discretization mathematical model of grid-connected inverter control is established in the dq frame. The corresponding relation between the control signal and the output current is deduced in formulas. Then, the values of structural parameters in the formulas can be obtained through iterative calculation, which can further reduce the amount of calculation. After several iteration cycles, the structural parameters are approximately equal to their actual values and the inverter can be controlled as an open-loop system with its dynamic performance optimized. At last, simulation and experiments are performed. The results show that the static performance of the proposed strategy is as good as that of the classical ones, but its dynamic performance is improved significantly.

A Novel Control Strategy for Grid-Connected Inverter Based on Iterative Calculation of Structural Parameters

The interval prediction of wind speed is crucial for the economic and safe operation of wind farms. To overcome the probability density function parameter optimization and long-term correlation of time series problems in an interval prediction method, a hybrid model based on the beta distribution of an allele real-coded quantum evolutionary algorithm (ARQEA) and a shared weight long short-term memory (SWLSTM) neural network is proposed for predicting the interval of short-term wind speed, which is beta–ARQEA–SWLSTM. Input variables are determined via autocorrelation functions, and the shape and position parameters in the beta distribution function are optimized by the ARQEA algorithm. An interval-divided multi-distribution function aggregation is proposed to deal with the fluctuation of wind speed series. Lastly, case studies are provided to demonstrate the effectiveness of the proposed method.

/pdf/interval-prediction-method-for-wind-speed-based-on-arqea-25ie9zay.pdf

Interval Prediction Method for Wind Speed Based on ARQEA Optimized by Beta Distribution and SWLSTM

Aiming at the problems of low prediction accuracy, long time, and poor results in current wind turbine generation power prediction methods, an offshore wind turbine generation power prediction method based on cascaded deep learning is proposed. Using deep belief networks, stacked autoencoding networks, and long short-term memory networks, a cascaded deep learning method is proposed to predict the power generation of offshore wind turbines. Multiple feature extractors are used to extract and fuse high-level features to form a unified feature with richer information to predict the power generation sequence of offshore wind turbines. According to the modeling strategy and port design strategy, using the stacked autoencoding networks as the basic unit, a cascaded deep learning model for generating power prediction of offshore wind turbines is established. Through the selection of input variables, the variables that have a great correlation with wind power are obtained. The layer-by-layer greedy algorithm is used for training from bottom to top, and supervised learning is used to fine-tune the network parameters from top to bottom to realize the generation power prediction of the offshore wind turbine. The experimental results show that the proposed method is effective in predicting the power generation of offshore wind turbines, which can effectively improve the prediction accuracy and shorten the prediction time.

Power Generation Prediction Method of Offshore Wind Turbines Based on Cascaded Deep Learning

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