System Parameter Variations Research Articles

Adaptive frequency control in smart microgrid using controlled loads supported by real-time implementation.

The operation of the system's frequency can be strongly impacted by load change, solar irradiation, wind disturbance, and system parametric uncertainty. In this paper, the application of an adaptive controller based on a hybrid Jaya-Balloon optimizer (JBO) for frequency oscillation mitigation in a single area smart μG system is studied. The proposed adaptive control approach is applied to control the flexible loads such as HPs and EVs by using the JBO which efficiently controls the system frequency. The suggested technique uses the power balance equation to provide a dynamic output feedback controller. The main target is to regulate the frequency and power of an islanded single area μG powered by a PV and a diesel generator with integrations of smart bidirectional loads (HPs and EVs) that are controlled by the proposed adaptive controller in presence of electrical random loads. Moreover, the JBO is designed to minimize the effect of the system load disturbance and parameter variations. For a better assessment, the proposed controller using JBO technique is compared with two other methods which are the coefficient diagram method (CDM) and adaptive one using classical the Jaya technique. In the obtained results, the frequency deviation is found as 0.0015 Hz, which is fully acceptable and in the range of the IEEE standards. The MATLAB simulation results reveal that the suggested technique has a substantial advantage over other techniques in terms of frequency stability in the face of concurrent disturbances and parameter uncertainties. The real-time simulation tests are presented using a dSPACE DS1103 connected to another PC via QUARC pid_e data acquisition card and confirmed the MATLAB simulation results.

Space-time exchanged symplectic integrator approach to temporal-mode selective optical three-wave mixing frequency conversion

A space-time exchanged symplectic integrator (SI) method is developed to solve the three-wave resonance interaction equations commonly used in describing a number of fundamental nonlinear optical phenomena, such as second harmonic generation, optical parametric amplification, etc. Exchanging the roles of the space and time coordinates allows us to solve these nonlinearly coupled equations with hard-wired boundary conditions using a highly accurate SI scheme instead of standard finite difference methods. The developed method is then applied to the optical three-wave mixing frequency conversion processes in which an input signal light is converted into an idler light guided by a strong pump pulse. The temporal-mode selectivity in the frequency conversion processes is examined by systematically varying system parameters and the results are summarized as contour maps of selectivity on the ‘group-slownesses’, defined by the inverse of the group velocities, of the signal pulse and of the idler pulse relative to that of the pump pulse. The areas showing the highest peaks on these undergo significant and characteristic changes in their positions as well as the heights for different values of the coupling strength γ. The observed trends are rationalized on the basis of the Schmidt modes obtained by decomposing the two-time transfer function that fully characterizes the dynamics of the signal–idler wave conversion.

Nonlinear Dynamic Bifurcation and Chaos Characteristics of Piezoelectric Composite Lattice Sandwich Plates

This paper investigates the nonlinear bifurcation and chaos characteristics of piezoelectric composite lattice sandwich plates at 1:3 internal resonance. The nonlinear vibration partial differential equation is first discretized to become an ordinary differential equation by applying the Galerkin method. The main resonance modulation equations and the parametric resonance for the external excitation frequency that is close to the system’s second-order modal frequency are then obtained by using the multiscale method. The Newton–Raphson method is subsequently applied to obtain the bifurcation diagram of the steady-state equilibrium of modulation equation with varying system parameters. The equilibrium stability is finally analyzed. We confirm the existence of static bifurcation such as saddle-node bifurcation, pitchfork bifurcation, and Hopf dynamic bifurcation. A detailed analysis is also conducted on the complex nonlinear jump phenomenon caused by the presence of multiple nonlinear steady-state solution regions. In the dynamic Hopf bifurcation interval, the fourth-order Runge–Kutta method is used to continue to track the dynamic periodic solution of the modulation equation in Cartesian coordinates. It is found that there are multiple boundary crises and attractor merging crises in the vibration system for piezoelectric composite lattice sandwich plates.

Smith Predictor Controller Design Using the Direct Synthesis Method for Unstable Second-Order and Time-Delay Systems

Industrial processes often involve a long time delay, which adversely affects the stability of closed-loop control systems. The traditional Smith Predictor (SP) is a model-based controller used in processes with large time delays. The variation of system parameters and load disturbance situations are disadvantages of the traditional SP, and researchers have, therefore, proposed modified SP structures. In this paper, a design method based on the direct synthesis approach on a modified SP structure is discussed. In the design, an I-PD controller structure is used on the set-point tracking side of the SP, and a cascading PD lead–lag controller is used on the disturbance rejection side. In contrast with other studies in the literature, the use of simpler controllers enables the mathematical expressions that arise in the direct synthesis method to be significantly reduced. The proposed method is examined under the disturbance input effects for normal and parameter-changing conditions on system models with unstable second-order plus time-delay processes. The first plant model has two unstable poles, the second has one stable and one unstable pole, and the third has one unstable and one zero pole. When the results obtained using the proposed method were compared with other methods, significant improvements were achieved in terms of set-point tracking, disturbance rejection, and robustness conditions.

Design, testing and implementation of a linear state variable feedback controller for providing tracking/regulation of a large platform of a Maglev vehicle

The basic parameters of an electromagnetic levitation systems are resistance of the coil, inductance of the coil, air-gap between magnet and the object, coil current, mass of the object, controller parameters and so on. The levitation systems are highly unstable and strongly non-linear. In real-life applications, these parameters are supposed to vary. A proportional–integral controller for the inner current control loop and a lead compensator for the position control loop have been designed and tested. The performance study under various parametric effects has been investigated broadly. Finally, both the stability for the proposed levitation system has been achieved with a better operating zone using linear state variable feedback controllers. The system dynamic performances have been recorded for variations of physical system parameters. The sensitivity/robustness of the set-up has been studied. This study will give an insight for designing any corrective measures and an overall idea to develop a levitation system.

Design of Vector Control Strategies Based on Fuzzy Gain Scheduling PID Controllers for a Grid-Connected Wind Energy Conversion System: Hardware FPGA-in-the-Loop Verification

This paper presents a hardware implementation upon a Field Programmable Gate Array (FPGA) of improved Vector Control Strategies (VCSs) based on a Fuzzy Logic System (FLS) of a grid-connected wind energy conversion system. Usually, the classical VCS is based on fixed-gain Proportional Integral Derivative (PID) controllers, which are known to exhibit limited performance against the nonlinear behavior of wind systems, such as rapid fluctuations of the wind speed and uncertainties of the system parameters. In order to overcome this limitation, an improved VCS based on Fuzzy Gain Scheduling PID controllers (VCS-FGS-PID) is suggested in this work to guarantee good tracking, high accuracy and good robustness under system parameter variations. Indeed, the PID controller gains are tuned, in real-time, by the FLS. In addition, the proposed VCS-FGS-PID methods are implemented on the FPGA in order to reduce the delays and the period of the system of the control loop, thanks to its parallel processing. In fact, the performance of the suggested VCS-FGS-PID approaches are proved by digital simulation with the Xilinx system generator tool under Matlab/Simulink, in addition to an experimental hardware-in-the-loop implementation using the FPGA. The obtained results demonstrate that the proposed VCS-FGS-PID techniques offer better performance as regards good tracking and high accuracy and good robustness against stator resistance variability compared to VCS-PI.

Statistical Majorana Bound State Spectroscopy.

Tunnel spectroscopy data for the detection of Majorana bound states (MBS) is often criticized for its proneness to misinterpretation of genuine MBS with low-lying Andreev bound states. Here, we suggest a protocol removing this ambiguity by extending single shot measurements to sequences performed at varying system parameters. We demonstrate how such sampling, which we argue requires only moderate effort for current experimental platforms, resolves the statistics of Andreev side lobes, thus providing compelling evidence for the presence or absence of a Majorana center peak.

A Synergistic Predictive Fusion Control Method and Application for Steering Feel Feedback of Steer-by-Wire System

In the steer-by-wire system of an intelligent vehicle, the steering feel feedback system provides accurate and real-time steering feel for a driver by precisely controlling the output torque of the steering feel feedback motor. The precision of output torque depends on the control effect of motor current, which is mainly affected by the key factors, including the dynamic performance of the control system, sampling error, and motor parameter variation. To provide accurate and real-time steering feel, how to design a control method considering the above influence factors has been an acknowledged challenging issue. To solve this problem, a synergistic predictive fusion (SPF) control method is proposed in this article. First, to improve the dynamic performance of the control system, the combined synergistic current control algorithm with the deadbeat predictive (DP) control principle and the synergistic predictive (SP) current control is proposed. Under this framework, a current correction algorithm is designed for the sampling error, and a weighting factor is obtained from the current ratio to adjust the weight of the sampling current. Meanwhile, considering the influence of parameter variation on current control error, an improved Adaline network parameter estimation algorithm is constructed to dynamically adjust motor parameters, and an input signal feedback adjustment step factor is added to enhance the parameter tracking ability. Finally, the simulation and test prove that using the proposed method can track steering feel more quickly (up to 10.61%) and more accurately (up to 13.56%) than using the traditional DP control method.

Low cost high performance self-starting sensorless single phase induction motor drive

<p><span lang="EN-US">The present paper is directed to achieve a low-cost high-performance self-starting single phase induction motor (SPIM) drive system. A phase shifted pulse width modulation (PWM) trains feeding the motor will replace the starting and running capacitors. Adaptive sliding mode control, enhance with model reference adaptive control (MRAC), is implemented to achieve high performance sensorless SPIM drive. The obtained results confirm the feasibility of the proposed system in starting and fast tracking the reference speed with nearly zero percentage overshoot and zero steady-state error. Moreover, the proposed SPIM drive system is robust to external load torque disturbances and insensitive to system parameter variations. Extensive simulations have been conducted to confirm the validity of the proposed system.</span></p>

Simple and Fast Voltage Disturbance Detection and Voltage Reference Generation Approach for Dynamic Voltage Restorer (DVR) to Compensate Unbalanced Voltage Sag and Swell in Three-Phase System: Simulation and Experimental Testing

The dynamic voltage restorer (DVR) is an important device for coping with voltage sag, swell, and unbalance. However, it is a real-time mechanism and must operate as quickly as possible to protect the sensitive and critical load from supply-side voltage anomalies. Hence, fast detection of voltage disturbances and reference voltage generation is essential for the DVR. This paper proposes a simple and fast voltage disturbance detection approach based on a simple three-phase phasor diagram which is easy to implement. The controller employed for the DVR uses a sliding mode control, making the entire system robust and insensitive to system parameter variations. Three different scenarios are simulated according to arbitrarily imposed supply conditions, verified in laboratory tests using the DSP R&D controller board. The results demonstrate a simpler, faster voltage-anomaly detection process which is easier for implementation compared to the other methods in the literature. The best and worst detection times (DTs) of the proposed approach are 1 and 4 ms, respectively. It yields a rapid response, accurate compensation, and robustness in restoring the load voltage to its nominal value within two cycles.

Cascaded Fractional Model Predictive Controller for Load Frequency Control in Multiarea Hybrid Renewable Energy System with Uncertainties

The rapid utilization of electricity has forced the stability of the power system for continuous operation. Due to difference in demand and generation, the frequency reference point changes that needs to be restored to its locus point for the stable operation of the power system. Therefore, a novel efficient control design is propounded to counter the phenomena of load frequency control. The designing of cascaded fractional model predictive controller coupled with fractional-order PID controller (CFMPC-FOPID) is designed for an efficient response of the power system under load disruption and system parameter variations. The controller is optimized by sooty tern optimization algorithm to identify the optimal parameters of the controller. The controller is tested under power mixing of renewable energy sources (i.e., PV and wind) and under varying load scenarios in multiarea hybrid power system. The proposed controller has effectively handled the frequency disruption under distinct load change by stabilizing it in 1.34 sec, 0.60 sec, and 0.41 sec for area-1 with an average time of 0.78 sec, and for area-2, the stabilizing time is 1.40 sec, 0.89 sec, and 0.56 sec with an average time of 0.95 sec, whereas the average time for MPC/PI, DSA-FOPID, GWO: PI-PD, and SCA: FOPI-FOPID is 7.67 sec, 4.68 sec, 1.77 sec, and 4.72 sec, respectively, for area-1 and 6.47 sec, 5.13 sec, 3.45 sec, and 5.02 sec, respectively, for area-2. The outcome result justifies the superiority of the studied technique.

Fractional order [formula omitted] controller for microgrid power system using cohort intelligence optimization

Microgrids are the future of energy management systems. Effective deployment and integration of microgrids can provide energy independence to many communities. The effectiveness of a microgrid primarily depends on its stability and reliability. Fractional regulators offer promising solutions to ensure stable microgrid performances. This article proposes a Fractional order Proportional–Integral–Derivative (FOPID) based secondary Load Frequency Control (LFC) for single area and dual-area AC microgrid systems. Cohort Intelligence (CI) based optimization was employed to tune the five major gain values of fractional PID regulators. The performance of the proposed CI tuned FOPID regulator was compared with that of the traditional PID controller tuned by Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) on the same single and dual control area microgrid systems. Four different cases of AC microgrid simulations were explored for LFC performance assessments and effective comparisons among different controllers and tuning algorithms. The first case considered load changes in single area AC microgrid with and without distributed energy system. The second case considered load variations in the two area AC microgrid system. The third and fourth cases included variations in random step load and system parameters respectively. Primary results affirm the superiority of the CI tuned FOPID regulator over its GA and PSO tuned PID counterparts in terms of its better steady-state precision, expressed in terms of the Integral Time Absolute Error (ITAE) selected as the objective function.

Piecewise model-based adaptive compensation control for high-speed trains with unknown actuator failures

Abstract In order to deal with the uncertainties caused by different operation conditions and unknown actuator failures of high-speed trains, an adaptive failures compensation control scheme is designed based on the piecewise model. A piecewise constant model is introduced to describe the variable system parameters caused by the variable operation environments, and a multiple-particle piecewise model of high-speed trains, with unknown actuator failures, is then established. An adaptive failure compensation controller is developed for the multiple-particle piecewise constant model, by using a direct model refering to the adaptive control method. Such an adaptive controller can not only compensate the uncertainties from unknown actuator failures, but also effectively deal with the uncertainties caused by different operating conditions. Finally, a CRH380A high-speed train model is taken as the controlled object for the simulation study. The simulation results show that the proposed controller ensures the desired system performance in the presence of unknown actuator failures and uncertain operation conditions.

A coupled fractional-order system with fluctuating frequency and its application in bearing fault diagnosis

In this paper, a coupled fractional-order system with fluctuating frequency driven by different periodic signals under various damping strength is investigated. Firstly, based on the Shapiro-Loginov formula and Laplace transform method, the expressions for the output amplitude gain (OAG) of the two subsystems are derived and the resonant behaviors of particles are analyzed. The OAG exhibits various resonance behaviors in response to variations in system parameters, input signals and dichotomous noise, including parameter-induced stochastic resonance, bona-fide resonance and stochastic resonance. Especially, the average behavior of the two output signals is synchronized when two subsystems’ input signals and damping strengths are equal, which is verified in the numerical simulation. Finally, the proposed system is applied to the bearing fault diagnosis to evaluate its engineering application value. The results prove that the system is effective in diagnosing fault signals and has excellent performance.

Routes toward chaos in a memristor-based Shinriki circuit.

In this paper, the complex routes to chaos in a memristor-based Shinriki circuit are discussed semi-analytically via the discrete implicit mapping method. The bifurcation trees of period-m (m = 1, 2, 4 and 3, 6) motions with varying system parameters are accurately presented through discrete nodes. The corresponding critical values of bifurcation points are obtained by period-double bifurcation, saddle-node bifurcation, and Neimark bifurcation, which can be determined by the global view of eigenvalues analysis. Unstable periodic orbits are compared with the stable ones obtained by numerical methods that can reveal the process of convergence. The basins of attractors are also employed to analyze the coexistence of asymmetric stable periodic motions. Furthermore, hardware experiments are designed via Field Programmable Gate Array to verify the analysis model. As expected, an evolution of periodic motions is observed in this memristor-based Shinrik's circuit and the experimental results are consistent with that of the calculations through the discrete mapping method.

Adaptive Model-Predictive-Control-Based Real-Time Energy Management of Fuel Cell Hybrid Electric Vehicles

To compete with battery electric vehicles, fuel cell (FC) hybrid electric vehicles (FCHEVs) are required to offer better performance in fuel economy and FC durability. To this end, this article proposes a novel real-time adaptive model predictive control (AMPC)-based energy management strategy (EMS) for FCHEVs to improve their fuel efficiency and mitigate the degradation of their onboard FC hybrid systems. First, a linear parameter-varying (LPV) prediction model of the FC hybrid system that considers the system parameter variation is developed. The model offers sufficient accuracy while enabling the real-time implementation capability of the AMPC. Then, an AMPC strategy is proposed to optimally distribute the load current of the FCHEV between the FC and the battery in real time. In each control interval of the AMPC, the LPV prediction model is updated online to adapt to the variations of the battery state of charge. The constrained optimization problem of the AMPC is then formulated to achieve a desired tradeoff among four performance metrics and is further transformed into a quadratic programming problem, which can be solved in real time. Hardware-in-the-loop tests are performed on a downscaled FC hybrid system with the proposed AMPC-based EMS, a commonly used rule-based EMS, an equivalent consumption minimization strategy, and an improved MPC-based EMS, respectively. Results show that among the four real-time EMSs, the AMPC-based EMS achieves the best performance in reducing hydrogen consumption and FC current fluctuation and the smallest optimality gap with respect to an offline dynamic programming-based optimal EMS.

Multi‐objective salp swarm algorithm‐based fractional order fuzzy precompensated PDPI controller for frequency regulation of hybrid power system

AbstractSignificant number of renewable sources when integrated with distributed generations and energy storage components form a sustainable power system called hybrid power system (HPS). But the stochastic nature of the sources and continuous variation of load cause serious issues in the power systems like frequency instability. Our work proposes a novel approach for load frequency control (LFC) of the proposed system. For the work, both frequency deviation and control signal are used to give the input signal to energy storage components. A new fuzzy‐based controller called fractional order fuzzy precompensated PDPI (FO‐FPPDPI) controller for LFC of the mentioned HPS is suggested in this paper. The controller is optimized by a nature inspired multi‐objective salp swarm algorithm (MSSA). The comparison of the proposed approach with the classical PID controller and Fuzzy PID (FPID) controller is carried out by taking several system operating conditions. Robustness of the FO‐FPPDPI controller is also validated for system parameter variations and the superiority of the proposed control strategy over PID and FPID controllers is validated for all the situations. Finally, the control strategy is realized in OPAL‐RT to measure its fidelity with the simulation results.

Seismic performance of a clutched inerter for structures with curved surface sliders

The performance of the clutched inerter in limiting the seismic response of structures isolated by curved surface slider (CSS) is investigated. The chosen structure is a flexible multi-floor building with an idealized linear shear-type behaviour. The equations governing the motion of an isolated structure with a clutched inerter under earthquake excitation have been derived. Initially, using the equivalent linearization technique, the isolated building's stationary floor absolute acceleration, isolator displacement, and device forces are evaluated. The stochastic earthquake excitation is considered to be a filtered white-noise Kanai-Tajimi model. The performance of the clutched inerter in mitigating the response of the isolated structure is studied by varying system parameters. The important parameters included are the inertance ratio of the clutched inerter, the period and friction coefficient of the CSS, and the flexibility of the superstructure. Further, there exists an optimum inertance of the clutched inerter for which the structure's highest floor absolute acceleration reaches its minimum level. The influence of important system parameters on the optimum inertance of the clutched inerter is also investigated. Finally, the performance of the clutched inerter as a supplemental device for the base-isolated structure is investigated when subjected to recorded earthquake motions consisting of near-fault as well as far-field types. The trends of results of isolated structures with clutched inerter under the recorded earthquake motions were in good agreement with that under stochastic excitation. It is demonstrated that the supplemental clutched inerter is effective in reducing the displacement response of base-isolated structures.

A Hybrid Grey Wolf Assisted-Sparrow Search Algorithm for Frequency Control of RE Integrated System

Nowadays, renewable energy (RE) sources are heavily integrated into the power system due to the deregulation of the energy market along with environmental and economic benefits. The intermittent nature of RE and the stochastic behavior of loads create frequency aberrations in interconnected hybrid power systems (HPS). This paper attempts to develop an optimization technique to tune the controller optimally to regulate frequency. A hybrid Sparrow Search Algorithm-Grey Wolf Optimizer (SSAGWO) is proposed to optimize the gain values of the proportional integral derivative controller. The proposed algorithm helps to improve the original algorithms’ exploration and exploitation. The optimization technique is coded in MATLAB and applied for frequency regulation of a two-area HPS developed in Simulink. The efficacy of the proffered hybrid SSAGWO is first assessed on standard benchmark functions and then applied to the frequency control of the HPS model. The results obtained from the multi-area multi-source HPS demonstrate that the proposed hybrid SSAGWO optimized PID controller performs significantly by 53%, 60%, 20%, and 70% in terms of settling time, peak undershoot, control effort, and steady-state error values, respectively, than other state-of-the-art algorithms presented in the literature. The robustness of the proffered method is also evaluated under the random varying load, variation of HPS system parameters, and weather intermittency of RE resources in real-time conditions. Furthermore, the controller’s efficacy was also demonstrated by performing a sensitivity analysis of the proposed system with variations of 75% and 125% in the inertia constant and system loading, respectively, from the nominal values. The results show that the proposed technique damped out the transient oscillations with minimum settling time. Moreover, the stability of the system is analyzed in the frequency domain using Bode analysis.

A Robust Controller Design based on Kharitonov’s Theorem for Frequency Control in an Interconnected Power System

The load frequency control of power systems is often carried out using methods that are dependent on the system load and parameters. Therefore, the controller design is not robust in unforeseen cases such an attack on the power system, variations in system parameters, or changes in load. In such methods, there is a need for an attack detection tool, and moreover, the controller parameters need to be adjusted as the load and power system parameters change. In this paper, Kharitonov's theorem was applied to design a robust decentralized load frequency control for a two-area power system in the presence of electric vehicle fleets as a power source that were targeted by a cyberattack. Furthermore, the robustness of the system against system nonlinearities was demonstrated by testing the efficacy of the controller on both linear and nonlinear systems. The controller design was robust such that there was no need to change the gains of the controller even during an attack. This was compared with the performance of controllers designed using GWO algorithm and fuzzy logic that needed retuning for different case studies with different variations in system parameters, load, or inclusion of a cyberattack to the electric vehicle fleets.