Transient Performance Improvement Research Articles

Probability-oriented disturbance estimation-triggered control via collaborative and adaptive Bayesian optimization for reentry vehicles

The paper investigates the performance improvement issue for reentry vehicles under uncertainties from the perspective of probability. The disturbance estimation-triggered control (DETC) proves to achieve transient performance increase compared with the standard disturbance-observer control methods, and the presented approach further exploits the probability-oriented transient performance improvement based on the collaborative and adaptive Bayesian optimization (CABO) technique, which constructs the main contribution of the paper. Based on the attitude dynamics of reentry vehicles, the DETC method is first introduced to guarantee the tracking stability and robustness against the uncertainties including the aerodynamic perturbation and wind effects. Meanwhile, the performance improvement is analyzed theoretically. Then, by virtue of the CABO algorithm, the CABO-based DETC is presented by combining the performance and probability indexes. Finally, the simulation results verify the effectiveness of the proposed control scheme and parameters influence is also discussed.

Leader-following consensus for linear multi-agent systems with transient performance improvement: A reset control approach

In this paper, we address the leader-following consensus problem of linear multi-agent system with improved transient performance. A state feedback controller incorporating a novel reset mechanism is proposed. It is shown that the convergence rate of the closed-loop system can be improved with the introduction of the reset mechanism. Furthermore, the regularity of the reset mechanism can be ensured for all agents, in contrast to previous studies where it was guaranteed only for one agent, which implies that the reset controller is bound to be triggered in finite time. Finally, an example is provided to verify the advantages of the proposed controller over some other kinds of control methods.

Analysis of transient energy conversion performance improvement of thermoelectric generator with penetrating crack under pulsed heat source

To simulate the performance of a thermoelectric generator (TEG) in practical applications more accurately and comprehensively analyse the influence of transient heat source and crack on such devices, it is necessary to examine the transient performance of TEGs with cracks under periodic pulsed heat source. In this study, a three-dimensional nonlinear dynamic finite element model with thermal, electrical, and mechanical coupling was established, and the laws describing the macroscopic electrical energy output and thermal stress distribution were derived. The position of the maximum thermal stress changed as the crack size varied. When the crack field axis was increased from 0.2 mm to 1.8 mm, the total output energy increased by 14.4 %, whereas the thermal stress increased to 447 MPa. The total energy growth rate under pulsed heat source conditions reached 52.3 % (from 62.71 J to 95.52 J) compared with that of the steady-state heat source. However, the maximum thermal stress increased by 173.6 % (from 91 MPa to 249 MPa). The results show that the crack and pulsed heat source can effectively improve the electrical output performance of TEGs; however, their mechanical properties deteriorated. This study is useful in terms of predicting the performance of TEGs in practical applications more accurately.

Deep Learning based Model Predictive Controller on a Magnetic Levitation Ball System

The magnetic levitation (maglev) ball system is a prototypical Single-Input-Single-Output (SISO) system, characterized by its pronounced nonlinearity, rapid response, and open-loop instability. It serves as the basis for many industrial devices. For describing the dynamics of the maglev ball system precisely in the pseudo linear model, the long short-term memory (LSTM) based auto-regressive model with exogenous input variables (LSTM-ARX) is proposed. Firstly, the LSTM network is modified by incorporating the auto-regressive structure with respect to sequence input, allowing it to deduce a locally linearized model without the need for Taylor expansion. Then, the LSTM-ARX model is transformed into a linear parameter varying (LPV) state space model, and upon this foundation, a model predictive controller (MPC) is proposed. Specifically, when deducing the MPC, the deep learning-based model is linearized by fixing its state input at the current state, so that the nonlinear, non-convex optimization problem can be converted to a finite-horizon quadratic programming problem, thereby deriving the explicit form of MPC. To further enhance the efficiency of the controller in real-time control tasks, a predictive functional controller (PFC) is proposed. It employs multiple nonlinear functions to fit the control sequence, thereby reducing the number of decision variables of the on-line optimization problem in MPC. The proposed controller was successfully applied to the real-time control of the maglev ball system. Simulation and real-time control experiments have validated the improvement in transient performance and efficiency of the LSTM-ARX model-based PFC (LSTM-ARX-PFC).

Data-Driven Decentralized Online System Identification-Based Integral Model-Predictive Voltage and Frequency Control in Microgrids

This article proposes a decentralized online system identification-based integral model-predictive control strategy for the voltage and frequency regulation in the microgrids (MGs). The main advantage of the proposed architecture is that the approach is inherently adaptive to the changing system conditions and does not require the knowledge of system parameters. In addition, as the decentralized system parameters are identified online utilizing local/global measurements, the controller is robust against unknown system disturbances. The decentralized nature of the proposed architecture is evolved from an extended Kalman filter that is utilized to synchronize the states of the identified model, thereby enabling a plug-and-play capability. The proposed controller is constructed utilizing the identified augmented incremental model that incorporates optimal integral action required to mitigate the steady-state errors in the MG voltage and frequency. Furthermore, controller formulation as a quadratic optimization problem including the constraints limits the control inputs within the bounds during electrical faults, thus assisting the downstream primary controller in accomplishing ride-through capability. The proposed framework is validated utilizing a section of the IEEE 123-bus distribution network for the different grid events including the effect of communication latency. Model-in-the-loop real-time simulation results demonstrate that the proposed framework offers significant transient performance improvement compared to the optimal proportional–integral strategy, especially around 50% faster regulation, when the communication latency is considered.

Multiple models for decentralised adaptive control of discrete‐time systems

AbstractThis paper investigates decentralised adaptive control of discrete‐time systems consisting of linear time‐invariant systems and a class of nonlinear systems. The parameters of the subsystems and the interconnection strengths between the subsystems are assumed unknown. Multiple adaptive prediction models with switching are used in this paper to address the relatively poor transient performance that typically results from a single prediction model. However, the application of this methodology requires indirect model reference discrete‐time adaptive control, and there appears to be very little focus in the literature on this. The principal contribution of this paper is to fill this void by arriving at proof of global stability of such decentralised adaptive systems using the theory of Lyapunov and properties of square‐summable sequences. The chosen representation of the system enables the same results to be directly applicable to both linear time‐invariant and linear‐in‐the‐parameters nonlinear subsystems. We consider two parametric update algorithms, one of which has a normalisation factor. Simulation studies included in this paper demonstrate the improvement in transient performance.

Transient performance improvement of DFIG‐based wind farm by H‐bridge fault current limiter

AbstractDespite the unique advantages a doubly fed induction generator (DFIG) offers to the grid‐integrated renewable energy systems, they have a limitation of being susceptible to grid fault as their stator windings are directly connected to the grid. The fault current limiters (FCLs) provide a sustainable solution by enhancing the fault ride‐through capability and thus it improve the transient performance of a DFIG. In this work, a multi‐inductor‐based H‐bridge fault current limiter (HBFCL) is proposed to augment the transient performance of a DFIG. The operational efficacy of the HBFCL is evaluated through the administration of both symmetrical and asymmetrical fault scenarios. The effectiveness of the HBFCL is further investigated by comparing the performance of the HBFCL with that of the bridge‐type series dynamic braking resistor (BSDBR). Both the graphical and numerical interpretations of the simulation result assert that the HBFCL improves the transient performance of a DFIG‐based wind farm and outweighs the performance of the BSDBR in all aspects.

Optimization DC-DC boost converter of BLDC motor drive by solar panel using PID and firefly algorithm

The use of solar photovoltaic panels as source of power for Brushless Direct Current (BLDC) motors requires a DC-DC Converter circuit. One application of solar energy is as a power source for Brushless Direct Current (BLDC) motors. The main problem is the voltage fluctuation and low DC voltage generated by the solar panel. This research aims to improve the performance of the DC-DC Boost Converter circuit and minimize voltage fluctuations. The methodology encompasses mathematical modeling of the circuit in the form of transfer functions and optimizing the DC-DC Boost Converter circuit using the Proportional Integral Derivative (PID) controller and the Firefly algorithm. Simulation testing results indicate an improvement in transient response performance of the DC-DC Converter circuit as a driver for the BLDC motor. This is evidenced by an increase in rise time from 499 s to 820 s, a decrease in settling time from 3.33 e+03 s to 2.07e+03 s, and a reduction in overshoot to 0 % from previously 11.4 %. The utilization of the firefly algorithm in optimization significantly enhances system efficiency, as demonstrated by faster achievement of stability without excessive oscillation and a reduction in the time required for the system to settle. Overall, this study shows that the firefly algorithm is effective in developing DC-DC Boost Converter circuits, improving system efficiency by reducing settling time and eliminating overshoot. These findings provide empirical evidence of the effectiveness of using artificial intelligence algorithms in enhancing the operational efficiency of energy conversion systems.

Decentralized State Estimation-Based Optimal Integral Model Predictive Control of Voltage and Frequency in the Distribution System Microgrids

The power distribution system is undergoing a shift from centralized to decentralized operation due to the large-scale integration of distributed energy resources (DER). To increase the scalability and reduce the dependency on communication networks, there is a strong need for a decentralized control framework that can maintain the nominal voltage and frequency in a microgrid. In this article, a novel decentralized state estimation-based optimal voltage and frequency restoration strategy is proposed. The framework computes the optimal integral model predicted control reference signal to the primary controller, that is unaffected by the local measurement noise, hence, ensuring stable power-sharing among the DERs. The proposed framework is built on a first-order DER model and a local/global measurements-based local state estimation technique, facilitating deployability to the grid edge devices. The capabilities of the proposed framework are tested and validated on a real-time simulator with a full system dynamic model considering an islanded section of the IEEE 123 node distribution network for the various grid events. It is observed that the framework offers significant transient performance improvement in comparison to the Linear Quadratic Regulator method, with around 70% faster voltage restoration when the communication latency is considered.

Transient and Steady-State Performance Improvement of IM Drives Based on Dual-Torque Model

Transient response performance and steady-state operation performance are the two most important performance indicators of a motor drive system. In order to solve these two problems, this study proposes a new induction motor (IM) model, and then designs a new simplified linearization controller method. First, the tangential force that determines the transient process of the motor is represented by electromagnetic torque, and the radial force is represented by reactive torque. Then, the dual-torque model of IM is derived, which not only accurately shows the rotating air-gap magnetic field through the amplitude and rotating angular frequency, but also visually demonstrates the physical essence of the transient process of IM. Then, this study proposes a simplified feedback linearization method without the analysis of zero dynamic. In addition, a time-scale hierarchical control system is designed to reduce the ripple caused by the coupling of different time-scale variables. The experimental results show that the steady-state torque ripple of the proposed method is 65% lower than that of RFOC, and the torque response speed is 10% higher than that of DTC.

Improvement of Transient Performance in Microgrids: Comprehensive Review on Approaches and Methods for Converter Control and Route of Grid Stability

Improvement of Transient Performance in Microgrids: Comprehensive Review on Approaches and Methods for Converter Control and Route of Grid Stability

Research and Improvement of Electromechanical Transient Performance of Power System Based on SIMULINK

The research on electromechanical transient stability of power systems is of great significance. Analyzing the parameters of the simulation system and its improved system with typical short-circuit faults, this paper studies the transient stability of power systems and its improvement. Firstly, based on the SIMULINK of MATLAB, a double-loop simulation with the single-machine-infinite-bus system is built. The simulation experiments of the system under the circumstances of three-phase short circuit and single-phase grounding short circuit faults are then carried out to analyze the parameters such as limit cutting time and voltage. Besides, this paper explores how the controllable series capacitor compensation device and reducing mechanical power output by prime mover improve the transient stability of the system. Experiments show that the stability after fault is under conditions where the fault is removed before the critical clearing time, which can increase the threshold to gain more response time for relay protection devices. Thus, the value of researching and improving system transient stability is proved.

Adaptive Neural Design of Consensus Controllers for Nonlinear Multiagent Systems Under Switching Topologies

Existing adaptive neural control methods for nonlinear multiagent systems (MASs) are only applicable under a fixed topology or are applicable under switching topologies but require some linear growth conditions on the nonlinear functions. Motivated by these limitations, a state-dependent adaptive neural design method is proposed in this article. Technically, our method is developed from a state-dependent Lyapunov function candidate, a switched control law, and a projection-based adaptation mechanism. To overcome the stability analysis difficulty caused by the new design of the Lyapunov function, a nonswitched compensation approach and a modified multiple Lyapunov functions method are proposed to derive a dwell-time condition, under which stability can be preserved. It is proved that in addition to stability, synchronization errors converge to a tunable residual around zero. Besides, the proposed scheme achieves the improvement of transient performance in terms of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula> norm and moreover, once there are no more topology switchings, asymptotic convergence of synchronization errors to a prescribed interval recovers automatically.

Transients Improvement in the Output-Feedback Adaptive Backstepping Control of Nonlinear Systems with Input Constraints

The paper addresses the problem of transient performance improvement in the output-feedback adaptive backstepping control of parametrically uncertain nonlinear system represented in the cascaded output-feedback form. The system state is inaccessible for measurement, the system input is constrained, and the control gain is assumed unknown (its sign is known). The problem is solved by applying specially modified K-filters with adjustable parameter, using the backstepping procedure taking into account the input constraints, and involving the Kreisselmeier-like adaptation algorithm with a modified swapping scheme. The algorithm enables us to improve the closed-loop transient performance without increasing initial “swings” in the control error transients. The latter property is achieved by including the high-order time derivatives of the adjustable parameters, calculated by the adaptation algorithm, in the control law. The properties of the closed-loop system are illustrated via simulation.

Transient Performance Improvement of Deadbeat Predictive Current Control of High-Speed Surface-Mounted PMSM Drives by Online Inductance Identification

This article presents an analytical derivation of transient performance deterioration for high-speed surface-mounted permanent magnet synchronous machine (SPMSM) drives under parameter mismatches, with an advanced deadbeat predictive current control (DBPCC), which exhibits fast dynamics and strong robustness with disturbances rejection. All the influential factors, including permanent magnet flux linkage and inductance mismatches, inverter nonlinearity, and speeds are taken into account in the analysis. It is shown that the transient performance with the advanced DBPCC is only dependent on inductance accuracy. Large transient overshoot and cross-coupling in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i> -axis currents at high speeds arise due to inductance mismatch. Based on the understanding gained through the analysis, a novel inductance identification method is also proposed in this article, which significantly improves the transient performance of high-speed SPMSM drives. The proposed online inductance identification method does not require signal injection, is nonintrusive, and can be easily implemented. By integrating the proposed method with the advanced DBPCC, the close-to-ideal deadbeat control can be achieved for high-speed SPMSM drives even in the presence of parameter mismatch and inverter nonlinearity. Both the analysis and proposed method have been validated experimentally on a prototype high-speed (30 000 r/min) SPMSM drive under various conditions.

Control Strategy for Resonant Inverter in High Frequency AC Power Distribution System with Harmonic Suppression and Transient Performance Improvement

In high frequency AC (HFAC) distribution system, the resonant inverter is used to improve power quality and keep the stability of the output AC voltage. Aiming at the problems of poor output power quality and slow transient performance caused by unreasonable filter parameter design and load change during inverter operation, a single-phase H-bridge LCLC resonant inverter based on analog circuit controller implement is introduced in this paper for HFAC power distribution system (PDS). In this study, to design harmonic compensator and analyze the responsiveness of the inverter, it is necessary to analyze the output voltage total harmonic distortion (THD) of LCLC resonant inverter and the performance of the open loop system in detail. On the one hand, a proportional-integral-resonant (PIR) controller is designed to maintain the zero static error of the voltage output and suppress the output voltage THD of LCLC resonant inverter. On the other hand, an integral controller combines with phase-shift modulation (PSM) method is presented to effectively improve the transient performance of resonant inverter and provide the fixed frequency of the output voltage. On the basis of the above, the experimental prototype is implemented with the output AC voltage root mean square of 28 V, and the output voltage frequency for resonant inverter is equal to switching frequency. A rated output power of 130 W experimental platform is built to verify the effectiveness of the theoretical analysis, control strategy, and modulation method.

Improvement of VSG Transient Performance Based on Power Feedforward Decoupling Control

Improvement of VSG Transient Performance Based on Power Feedforward Decoupling Control

Modified hierarchical strategy for transient performance improvement of the ORC based waste heat recovery system

Organic Rankine Cycle (ORC) system outperforms in the low-grade waste heat recovery and has found its wide application in engineering practice. Due to the strong coupling multivariable nature of the ORC power unit, the traditional hierarchical control scheme, which is composed of real-time optimization (RTO) in the upper layer and model predictive control (MPC) in the lower one, is usually utilized in the control of the ORC system for good economic performance. The upper RTO solves an economic objective function in real time and obtains the optimal steady-state setpoints which are sent to the lower MPC layer for tracking. However, the disadvantage of the traditional hierarchical control scheme lies in its neglection to the transient characteristics with the evolution of the ORC system, which will finally lead to poor overall control performance. To address this problem, in this article, a modified hierarchical control strategy which is denoted as RTO-PFFDMC grounded on pseudo feedforward dynamic matrix control (PFFDMC) algorithm is proposed. The novel PFFDMC presented in this article is applied to replace the traditional dynamic matrix control (DMC) in the lower layer and thus achieve the closed-loop reference trajectory of ORC system expected by field engineers as closely as possible without cumbersome adjustment of tuning parameters. Simulations are performed to verify the effectiveness of the proposed strategy on transient performance in comparison with the traditional one.

A switching-free multiple-model approach for adaptive FTC of non-Lipschitz nonlinear uncertain systems under persistent actuator failures

A novel actuator failure compensation scheme is proposed for affine nonlinear uncertain systems (not necessarily Lipschitz) subject to persistent/intermittent actuator faults/failures unknown in time, magnitude and pattern. The proposed control methodology satisfies the nonlinear separation principle through a modular backstepping control. The controller is then augmented with multiple estimation models to estimate failure induced parametric uncertainties and unknown system parameters. The output transient performance at start up and post-failure instances, is improved on account of a two layer adaptation which enhances the convergence speed and accuracy of parameter estimates. The proposed fault tolerant control (FTC) method yields a faithful accommodation of uncertain finite as well as infinite/intermittent/persistent actuator failures while ensuring satisfactory output transient and steady state performances. Further, compared to existing multiple model based adaptive fault tolerant control design for nonlinear systems, the proposed methodology circumvents the issues of stability due to switching between different models and utilizes a minimum number of estimation models for parameter estimation without compromising on the output performance. Consequently, the computational burden is also reduced. Compared to multiple model adaptive control based FTC strategies proposed earlier which assume finite actuator failures and Lipschitz nonlinear system, the proposed method is applicable to both Lipschitz and non-Lipschitz nonlinear systems affected by intermittent actuator failures. Using the concepts from stability analysis in random nonlinear impulsive systems, the L∞ and L2 bounds on tracking error for all future time are derived in the case of intermittent/persistent actuator failures obtained using the proposed fault-tolerant controller. The improvement of output transient performance in the proposed control scheme in comparison with controller with single identifier, is theoretically proved and quantified.

Performance enhancement and power management strategy of an autonomous hybrid fuel cell/wind power system based on adaptive neuro fuzzy inference system

In this paper, a hybrid wind/fuel cell generation system which can be used for loads in remote areas as a micro grid application is considered. This micro grid mainly includes fuel cell (FC), wind generator as electrical power suppliers, resistive-inductive impedance as static load, induction motor (IM) as a dynamic load, DC/AC converter and water electrolizer for supplying hydrogen gas. The Fuel cell is used to compensate the decrease in the power generated by wind, which leads to an increase in the system efficiency. Furthermore, an adaptive control model and achievement refinements of a micro-grid using Adaptive Neuro Fuzzy Inference System (ANFIS) controller has been utilized to regulate the load voltage and frequency. This suggested microgrid system is achieved so that the wind generation unit supplies the loads, while any additional energy needed by the loads will be offset by the fuel cell generator unit. Thus, the main objective of this work is to apply an adaptive control method for improving the proposed electrical micro grid performance. In addition, the performance of the considered system is compared with the proposed ANFIS control when applying the traditional fuzzy control. The outcomes also demonstrated a better reaction and durability to the chosen control model. The MATLAB/SIMULINK programming software tools have been used for carrying out case studies towards the evaluation and validation of the methodology developed in this work with applications. The proposed solution achieved improvement in transient performance. However, the settling time is decreased to 21% in the case of using the suggested ANFIS controller comparing with conventional fuzzy control.