Lead-lag Controller Research Articles

Deep reinforcement learning for tuning active vibration control on a smart piezoelectric beam

Piezoelectric transducers are used within smart structures to create functions such as energy harvesting, wave propagation or vibration control to prevent human discomfort, material fatigue, and instability. The design of the structure becomes more complex with shape optimization and the integration of multiple transducers. Most active vibration control strategies require the tuning of multiple parameters. In addition, the optimization of control methods has to consider experimental uncertainties and the global effect of local actuation. This paper presents the use of a Deep Reinforcement Learning (DRL) algorithm to tune a pseudo lead-lag controller on an experimental smart cantilever beam. The algorithm is trained to maximize a reward function that represents the objective of vibration mitigation. An experimental model is estimated from measurements to accelerate the DRL’s interaction with the environment. The paper compares DRL tuning strategies with [Formula: see text] and [Formula: see text] norm minimization approaches. It demonstrates the efficiency of DRL tuning by comparing the control performance of the different tuning methods on the model and experimental setup.

Utilization of G-Programming Language for Educational Control Application: Case Study of Magnetic Levitation of Elastic Beam

This paper explores the application of G-Programming tools in designing and implementing traditional control algorithms for a magnetic levitation system using LabVIEW. The study focuses on the challenges of managing this fast, dynamic, and sensitive system, demonstrating the effcacy of LabVIEW G-Programming for control education. PID and Lead-Lag controllers are developed within LabVIEW, featuring tuning and optimization capabilities through Virtual Instruments of the control design and simulation toolkit. The research provides insights into the system’s modelling, control action testing, dynamic simulation, and real-time control law deployment, concluding that G-Programming effectively facilitates experiential learning and validation in control systems engineering.

Control strategy of a piezoelectric stage based on the parameter-dependent Bouc-Wen model under condition variations

This paper proposed a feedforward-feedback (FF-FB) control strategy for a linear piezoelectric walking stage (LPWS) based on the parameter-dependent Bouc-Wen (PD-BW) model under condition variations. The mathematical model of LPWS is established by linearization, which consists of a linear system and a hysteresis nonlinearity system. The linear dynamics model of LPWS is identified by the swept-sine method, whereas the hysteresis nonlinearity model of LPWS is identified based on the PD-BW model. The feedforward controller based on the inverse PD-BW model can compensate for the inherent hysteresis nonlinearity of LPWS at varying excitation frequency, surrounding temperature, and external load. A proportional plus lead-lag feedback controller is built to reduce feedforward modelling error and improve the output accuracy of LPWS. The sensitivity analysis of the LPWS feedback control system is performed to reduce the effect of disturbance and noise. Experimental results present the feasibility and effectiveness of the proposed FF-FB control strategy to achieve an efficient and accurate positioning performance.

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.

Online Transfer Function Estimation and Control Design Using Ambient Synchrophasor Measurements

This paper proposes an adaptive control design framework for damping inter-area oscillations in power systems. The design is entirely based on ambient synchrophasor measurements. The proposed framework consists of three components, namely, input-output transfer function estimation, channel selection, and feedback control implementation. For the first part, we propose a simple yet effective novel estimation algorithm in the frequency domain to identify low-order transfer functions in pre-specified frequency ranges between the measured input and output data using ambient synchrophasor measurements. In the second part, based on the identified transfer functions, the joint controllability-observability (JCO) of all identified channels is estimated and most suitable control candidates for damping the target inter-area mode are selected. Finally, an appropriate lead-lag controller is designed using a classical frequency-domain method to improve the damping of a dominant oscillatory inter-area mode. Since ambient data is used in the analysis, the selected channel as well as the designed controller parameters can be updated online whenever the system operating point changes resulting in an efficient adaptive controller. The effectiveness of the proposed framework is illustrated by implementing it on the two-area Kundur test system and the IEEE 39-bus test system.

Chaotic Harris hawks optimisation based fuzzy lead lag TCSC and PSS with coordinated control design for enhancement of power system transient stability

This paper investigated the application of Chaotic Harris Hawks optimisation (CHHO) technique for the tuning of a coordinated control of fuzzy lead lag based TCSC controller with fuzzy based PSS power system. The eigenvalues and simulation results of proposed CHHO optimised TCSC controller are presented and compared with a coordinated control of lead lag TCSC controller with PSO, DE and GSA optimised lead lag controller under various cases of operating conditions and disturbances in SMIB power system. The CHHO optimised of proposed fuzzy coordinated controller is compared with CHHO based lead lag TCSC coordinated control of same power system to check the effectiveness and robustness analysis. Finally, the proposed design approach is extended to multi-machine test model system to demonstrate the coordinated control of fuzzy lead lag TCSC damping controller with fuzzy power system stabilises to damp out oscillations in power system to improve transient stability performances.

Improved Performance in the Control of DC-DC Three-Phase Power Electronic Converter Using Fractional-Order SMC and Synergetic Controllers and RL-TD3 Agent

In this article, starting from a benchmark represented by a Direct Current-to-Direct Current (DC-DC) three-phase power electronic converter used as an interface and interconnection between the grid and a DC microgrid, we compare the performances of a series of control structures—starting with the classical proportional integrator (PI) type and continuing with more advanced ones, such as sliding mode control (SMC), integer-order synergetic, and fractional-order (FO) controllers—in terms of maintaining the constant DC voltage of the DC microgrid. We present the topology and the mathematical modeling using differential equations and transfer functions of the DC-DC three-phase power electronic converter that provides the interface between the grid and a DC microgrid. The main task of the presented control systems is to maintain the DC voltage supplied to the microgrid at an imposed constant value, regardless of the total value of the current absorbed by the consumers connected to the DC microgrid. We present the elements of fractional calculus that were used to synthesize a first set of FO PI, FO tilt-integral-derivative (TID), and FO lead-lag controllers with Matlab R2021b and the Fractional-order Modeling and Control (FOMCON) toolbox, and these controllers significantly improved the control system performance of the DC-DC three-phase power electronic converter compared to classical PI controllers. The next set of proposed and synthesized controllers were based on SMC, together with its more general and flexible synergetic control variant, and both integer-order and FO controllers were developed. The proposed control structures are cascade control structures combining the SMC properties of robustness and control over nonlinear systems for the outer voltage control loop with the use of properly tuned synergetic controllers to obtain faster response time for the inner current control loop. To achieve superior performance, this type of cascade control also used a properly trained reinforcement learning-twin delayed deep deterministic policy gradient (RL-TD3) agent, which provides correction signals overlapping with the command signals of the current and voltage controllers. We present the Matlab/Simulink R2021b implementations of the synthesized controllers and the RL-TD3 agent, along with the results of numerical simulations performed for the comparison of the performance of the control structures.

A Coordinated Pumped Storage Dual Compensated Hydro Governor with PSS Action to Damp Electromechanical Power Oscillations

Subject to increasing penetrations of renewable sources like solar photovoltaic (SPV) and wind energy sources, power system oscillation damping is going to be a critical challenge for system operators. This work proposes a new dual compensated governor (DCG) in coordination with a power system stabilizer (PSS) of a pumped storage hydro plant for power oscillation damping subject to intermittent SPV and wind penetration for a hydro, wind, and SPV integrated power system. The phase lag provided by the hydro governor is compensated by additional phase lead contributed by the dual compensation, where speed and real power deviations brought by uncertain SPV and wind penetrations are simultaneously controlled by two lead-lag controllers before being applied to conventional Proportional-Integral-Derivative (PID) governor. Again, subject to critical oscillatory unstable conditions, the DCG is coordinated with PSS through a multiobjective function employing a new modified Differential Evolutionary-Particle swarm optimization (MDEPSO) algorithm. Different case studies with sudden and random SPV and wind penetrations being executed with the proposed controller considering a two area four machine and 39 bus multimachine system with pumped storage hydro units to observe system oscillations are considered. The proposed damping control action has been implemented to damp these oscillations, and the damped response has been analyzed with eigenvalue distributions and Bode plots with sensitivity analysis. The proposed action is found to be much more efficient in contrast to conventional PID governor and PSS damping action. Also, the usage of present hydro governors can be much improved by this coordinated controller action.

Modified grasshopper optimization algorithm optimized adaptive fuzzy lead-lag controller for coordinated design of FACTS controller with PSS

This paper proposes adaptive fuzzy lead-lag controller structures for power system stabilizer and flexible AC transmission system based damping controllers to increase the stability of power system. The parameters of the proposed controller are tuned by a modified grasshopper optimization algorithm (MGOA). The new algorithm named MGOA accomplishes a proper balance between exploration and exploitation phases of original grasshopper optimization algorithm. This capability of MGOA is certified by using the benchmark functions by comparing with that of a grasshopper optimization algorithm, genetic algorithm, evolutionary strategies, particle swarm optimization, bat algorithm, population based incremental learning, flower pollination algorithm, monarch butterfly optimization and improved monarch butterfly optimization. The proposed controller is optimized and verified under various loading circumstances using MGOA method. The results of MGOA are compared with grasshopper optimization algorithm, genetic algorithm, and particle swarm optimization. Additionally, the results of the proposed MGOA are compared with conventional lead-lag controller to demonstrate its superiority.

Coordinated Design of Type-2 Fuzzy Lead–Lag-Structured SSSCs and PSSs for Power System Stability Improvement

This work suggests a type-2 fuzzy lead–lag (T2FLL) controller structure for flexible AC transmission system (FACTS)-based damping controllers and power system stabilizers (PSSs) for power system stability improvement. The values of the suggested controller are optimized by a hybrid adaptive differential evolution and pattern search algorithm (hADE-PS) method. Initially, a single-machine infinite-bus (SMIB) system with lead–lag (LL)-structured FACTS and PSS controllers is considered, and the dominance of the hADE-PS method is established over the original differential evolution (DE), genetic algorithm (GA), and particle swarm optimization (PSO). The supremacy of T2FLL over the lead–lag (LL) controller is established under different large and small disturbance conditions, as well as varied loading conditions and fault positions. Lastly, the effectiveness of T2FLL is evaluated in a multimachine power system (MMPS). It is demonstrated that the suggested T2FLL offers better performance than the LL controller under various large and small disturbance conditions by providing significantly more damping to all modes of oscillations.

Individual pitch control by convex economic model predictive control for wind turbine side-side tower load alleviation

The wind turbine side-side tower motion is known to be lightly damped. One viable active damping solution is realized by deploying individual pitch control (IPC) such that counteracting blade forces are created to alleviate the tower fatigue loading caused by this motion. Existing IPC methods for side-side tower damping in the literature, such as linear quadratic regulator and lead-lag controller, cannot accommodate direct optimization and tradeoff tunings of the wind turbine economic performance. In this work, a novel side-side tower damping IPC strategy under a convex economic model predictive control (CEMPC) framework is therefore developed to address these challenges. The main idea of the framework lies in the variable transformation in power and energy terms to obtain linear dynamics and convex constraints, over which the economic performance of the wind turbine is maximized with a globally optimal solution in a receding horizon manner. The effectiveness of the proposed method is showcased in a high-fidelity simulation environment under both steady and turbulent wind cases. Lower fatigue damage on the side-side tower bending moment is attained with an acceptable level of pitch activities, negligible impact on the blade loads, and minor improvement on the power production.

Design of a Damping Controller Using a Metaheuristic Algorithm for Angle Stability Improvement of an MIB System

Low frequency oscillations in large power systems may result in system instability under large disturbances. Power system stabilisers (PSS) play an effective role in damping these low frequency oscillations by injecting a modulating signal in the excitation loop of a synchronous machine. A new metaheuristic optimisation algorithm termed the sine cosine algorithm (SCA) was proposed for optimising PSS controller parameters to obtain an optimal solution with the damping ratio as an objective function. The SCA technique was examined on a single machine infinite bus (SMIB) system under distinct loading situations and matched with a moth flame optimisation technique and evolutionary programming to design a robust controller of PSS. The simulation was accomplished using a linearised mathematical model of the SMIB. The performance of a designed lead lag-controller of PSS was demonstrated using eigenvalue analysis with simulations, showing promising results. The dynamic performance was validated with respect to the damping ratio, the eigenvalue’s location in the s-plane and rotor angle deviation response to demonstrate system stability.

A Neuro-Adaptive Control Scheme to Improve Dynamic Stability of Wind Power System using Battery Energy Storage

This paper investigates the capability of the battery energy storage system (BESS) to enhance the stability of wind power systems using an improved intelligent control strategy based on neural identification and simultaneous control. The proposed neural identifer comprises of only a few adaptive parameters, which reduce the computational burden and make it easier to implement. It faithfully estimates the local linear model of a dynamic system and adjusts its parameters online for varying operating states of the system. The proposed neuro-adaptive controller needs local measurements only and is model-free. The performance of the proposed controller is compared with the lead-lag controller whose parameters are tuned using teacher learning-based optimization. The performance of the controllers is compared on the basis of various power system stability indices such as oscillation damping, wind-power smoothing and improving the voltage profile of the wind farm under varying operating conditions.

An Adaptive Neural Network-Based Controller to Stabilize Power Oscillations in Wind-integrated Power Systems

This paper proposes an improved neuro-adaptive control scheme, based on online system identification and simultaneous control, for damping the low-frequency oscillations generated due to the wind integration in conventional power systems. A simple, linear neural identifier, with a few adjustable connection weights, is used which ensures minimal computational burden and fast learning capability in comparison with other neuro-controllers having highly complex and non-linear architecture, which may pose difficulties in their real-time implementation. It is demonstrated that a simple linear neural identifier, which approximates a local linear model of a system, by adjustment of its parameters online, is faithfully able to track the varying dynamics of the system. The proposed controller is used as a supplementary control in the rotor-side converter of the doubly-fed induction generator. Improved oscillation-damping performance over a wide range of operating conditions, in comparison with a well-designed genetic algorithm-based conventional lead-lag controller, has been validated through simulation studies carried out on wind-integrated-inter-connected power system. Moreover, the proposed control scheme is model free, easier to implement and needs local measurements only.

Advanced control to damp power oscillations with VSC‐HVDC links inserted in meshed AC grids

In this article, we consider the particular situation of a HVDC link inserted in a meshed AC grid, which has interarea modes at upper frequencies than the usual ones. In this case, the standard IEEE (lead-lag plus gain) controllers may not be efficient. As a matter of fact, in this grid situation, the control is challenged by unstable zeros and system uncertainties, systematically put into evidence. Two original robust strategies based on matching a reference model using LMI optimization approach are designed to provide coordinated active and reactive power modulation for the HVDC link. They are based on a reduced order control model of the HVDC and the neighboring AC zone, which integrates the dynamics of interest. Besides increased damping and diminished impact of the unstable zeros, this new control achieves robustness against grid variations. Investigations with both linearized and nonlinear model of the system are carried out to settle and validate the approach. The efficiency and robustness of the proposed controllers are tested and compared with the ones of several standard controllers on a realistic benchmark of 19 generators interconnected by a meshed AC grid.

Phase Lead-Lag Controller Design for Real-Time DC Motor Position Control with Root Locus and Bode Diagram

Bu çalışmada, gerçek zamanlı DC motor konum kontrolü için klasik kontrol yöntemlerinden olan faz ilerlemeli ve faz ilerlemeli – gerilemeli denetleyici tasarımı kök yer eğrisi ve Bode diyagramı yöntemleri ile gerçekleştirilmiştir. Model tabanlı bir denetleyici tasarlanacağı için matematiksel model, kara kutu sistem tanımlama yöntemiyle deneysel verilerden hesaplanarak elde edilmiştir. STM32F4 geliştirme kiti ve Matlab destekli Waijung blok seti kullanılarak uygulama geliştirilmiştir. Denetleyici performansı, sisteme referans girişler verilmesi sonucu deney düzeneği üzerinde gerçek zamanlı sonuçların gözlenmesi ile test edilmiştir. Her iki yöntem tasarım sürecinin işleyişi ve yöntemlerin uygulanabilirliği gerçek zamanlı olarak gösterilmiş ve tasarlanan denetleyicilerin başarılı performans sergilediği gözlenmiştir. Gerçekleştirilen uygulamada kullanılan test düzeneği ve yöntemler, kontrol uygulamaları için hem güvenilir çalışma hem de ekonomik çözüm sunmaktadır.

Optimal Active and Reactive Power Dispatch and Excitation System Controller Design Using Genetic , Algorithm.(Dept.E)

Power system economical operation consists of two aspects: active power regulation and reactive power dispatch. This multiobjective optimization problem is solved in this p ap er as two subproblems: P- and Q-problems. The solution is applied to an EEE 14-bus system using Genetic Algorithm (GA). Additionally, An exciter system lead-lag controller and feedback compensator parameters are also optimally selected using GA.

Design of a Damping Controller Using the SCA Optimization Technique for the Improvement of Small Signal Stability of a Single Machine Connected to an Infinite Bus System

This paper discusses the use of a stochastic metaheuristic population-based optimization algorithm known as the sine cosine algorithm (SCA) to design the parameters of a power system stabilizer (PSS) for damping electromechanical oscillations in a single machine connected to a large power system. The design of the PSS parameters was formulated as an optimization problem to minimize the objective function value. The SCA was used to obtain the best values of the PSS parameters under the objective function. Simulation was carried out by a linearized power system model. The lead lag controller was used as the PSS structure and the results from that were compared with those obtained by moth flame optimization and evolutionary programming. The results showed that the SCA is more effective than are the other techniques in exploration and exploitation to tune the PSS parameters and enhance the power system stability by damping oscillations in a range of loading conditions.

Optimal damping for generalized unified power flow controller equipped single machine infinite bus system for addressing low frequency oscillation

Low frequency oscillation (LFO) is one of the major concerns for reliable operation of the power system. This LFO occurs due to the failure of the rotor to supply sufficient damping torque to compensate the imbalance between mechanical input and electrical output. Hence, in this paper, we adopt a third generation flexible AC transmission system (FACTS) device named generalized unified power flow controller (GUPFC) based damping controller in order to investigate its effect for mitigating LFO for an single machine infinite bus (SMIB) system. To find an effective damping controller-optimizer pair, we integrate proportional–integral (PI) or lead–lag as a controller and grey wolf optimizer (GWO), differential evolution (DE), particle swarm optimization (PSO), whale optimization algorithm (WOA), and chaotic whale optimization algorithm (CWOA) as an optimizer. Later, we investigate the performances for the above mentioned controller-optimizer pairs through time domain simulation, eigenvalue analysis, nyquist stability test, and quantitative analysis. Moreover, we carry out two non-parametric statistical tests named as one sample Kolmogorov–Smirnov (KS) test and paired sample t−test to identify statistical distribution as well as uniqueness of our optimization algorithms. Our analyses reveal that the GWO tuned lead–lag controller surpasses all other controller-optimizer combinations.

STATCOM Parameters Optimization Using Multi-Objective LAPO for Enhancing the Stability of Combined Wind Farm

This paper deals with the simultaneous tuning of proportional integral controller of static synchronous compensator (STATCOM) associated with lead-lag controller (LL) in order to improve the stability performance of combined wind farm (CWF). This type of farms has been developed to summarize the main benefits of Squirrel-Cage Induction Generator (SCIG) and doubly fed induction generators (DFIG). The performance of CWF with the tuned STATCOM is studied during line-to-line and three-phase faults. Multi-objective Lightning Attachment Procedure Optimization Algorithm (LAPO) is used as an intelligent optimization technique to determine the control parameters which can achieve the specified objective functions; minimizing the error signal and maximizing the output signal. The multi-objective LAPO is used to obtain the optimized values of AC voltage regulator, DC voltage regulator, current regulator, gains and time constant of LL. The ability of STATCOM tuned by multi-objective LAPO is examined by studying the performance of three wind farms. The first one is CWF and two other wind farms; one of them is based only on SCIG (SCIG-WF) and the other based only on DFIG (DFIG-WF) when they associated with the tuned STATCOM LAPO during grid fault. The results show that CWF associated with the tuned STATCOM LAPO has the best performance during grid faults compared with the other wind farms.