Adaptive Damping Controller Research Articles

Adaptive Damping Control of MMC to Suppress High-Frequency Resonance

Adaptive Damping Control of MMC to Suppress High-Frequency Resonance

Adaptive Damping Control to Enhance Small-Signal Stability of DC Microgrids

This paper proposes an adaptive active control approach for damping the low-frequency oscillations in a DC microgrid (DC-MG). The DC-MG is comprised of hybrid power sources (HPSs) formed by a parallel set of supercapacitor modules and photovoltaic systems. The HPS controller includes a multi-loop voltage controller for adjusting the DC-MG voltage and a virtual impedance loop for damping current oscillations. The virtual impedance loop is augmented to the inner loop of the voltage controller. An adaptive tuning strategy is developed to adjust the damping coefficient of the virtual impedance loop optimally. In the tuning process, small-signal analysis is used to determine an initial adjustment for the damping coefficient. Subsequently, an approach based on intelligent neural network is intended to provide accurate online correction of the damping coefficient, which passes the dependency of the converter control system on the operating point conditions and accommodates different operation conditions. A sensitivity analysis is also conducted to investigate the effects of the system parameters on the HPS stability. Moreover, a mesh analysis is carried out to examine the stability of low-frequency modes of the whole DC-MG using the proposed control scheme. Case studies are conducted to demonstrate the performance of the proposed control strategy, and the analysis results are verified by Hardware-in-the-Loop (HIL) setup using OPAL-RT (OP5600) and dSPACE (DS1202) simulators.

A grid-side multi-modal adaptive damping control of super-/sub-synchronous oscillations in type-4 wind farms connected to weak AC grid

• A multi-modal damping control (MADC) is proposed to damp multiple oscillation modes independently. • A general design framework for damping multiple oscillation modes is given. • MADC is validated to damp sub-synchronous and super-synchronous oscillations in Type-4 wind farms. Type-4 wind farms connected to weak AC grids have the risk of sub-synchronous oscillation/interaction (SSO/SSI) with multiple oscillation modes, which can lead to unplanned outages and system destabilization. This paper first presents the magnitude and frequency characteristics of the oscillations followed by the control design requirements. Then, it gives a general design of grid-side multi-modal adaptive damping control (MADC) to stabilize multiple oscillation modes. The MADC utilizes an FFT-based inter-harmonic phasor measurement algorithm, which can detect and track multiple oscillation modes simultaneously. The MADC uses bus voltages and line currents as inputs to extract and control each oscillation mode without considering the coupling of the modes due to the frequency coupling effect. The unstable modes are stabilized by injecting phase-shifted currents with multiple frequencies such that the impedance response of the whole system around the concerned frequencies is reshaped. Notable features of the proposed damping control include automatic and independent detection, tracking and suppression of multiple oscillation modes. The proposed MADC's damping performance is validated through extensive electromagnetic transient (EMT) simulations of type-4 wind farms connected to a weak ac grid causing super-/sub-synchronous oscillations. The system model and the proposed damping control are designed and tested in commercial EMT software.

Improved Data-Driven Model-Free Adaptive Damping Controller Design for Interconnected Power Systems With Stochastic Communication Delays

The original model-based wide-area damping control (WADC) is difficult to obtain good damping performance due to the complex structure of interconnected power systems. In this paper, an improved data-driven model-free adaptive control (ID-MFAC) algorithm is proposed to design WADC for interconnected power systems with stochastic communication delays. First, the multiple-input multiple-output power system can be decoupled as multiple single-input single-output subsystems by considering controller interactions and system disturbances. Second, a step-size vector is designed to adjust the pseudo-partial derivative and make the algorithm more flexible by considering both input and output data adequately. Furthermore, an adaptive delay compensator is designed to compensate stochastic communication delays. Last, simulations are conducted to verify that the ID-MFAC algorithm can efficiently suppress multiple inter-area oscillations modes under various stochastic communication delays.

Oscillation Damping of Integrated Transmission and Distribution Power Grid With Renewables Based on Novel Measurement-Based Optimal Controller

Renewable energy resources are gaining fast adoption in the power grid because of their relatively low cost and offered environmental benefits. However, the grid will experience decreased inertia as a result of these added resources. Numerous research studies have focused on improving the grid damping, including the lack of inertia, due to renewable resource integration. In this article, we present an adaptive damping controller to help mitigate oscillations during disturbances on the grid, caused by fault occurrences, sudden load changes, capacitor switching, or even the intermittent nature of the energy sources for most renewable systems, considering both integrated transmission and distribution systems. The test system is developed based on Kundur’s two-area system as the transmission system and the IEEE 123-bus system as the distribution system. Then, the novel proposed adaptive optimal damping control architecture is validated via simulation-based test cases conducted in MATLAB/Simulink using real-life grid data. It is observed that the proposed control architecture not only damps the oscillations more than 10% as compared with the state-of-the-art methods but can also control multiple generators.

Adaptive Damping Control of Subsynchronous Oscillation in DFIG-Based Wind Farms Connected to Series-Compensated Network

The frequency of subsynchronous oscillation (SSO) in doubly-fed induction generator (DFIG)-based wind farms connected to series-compensated networks is determined by system-wide operating conditions, including the wind speed, number of in-service wind turbines, and degree of series-compensation. This paper proposes an adaptive mitigation scheme to damp the SSO for a full range of subsynchronous frequency variations due to different operating conditions. The proposed scheme comprises 1) a subsynchronous frequency estimator (SSFE) and 2) an adaptive subsynchronous damping controller (ASDC). The SSFE detects the SSO mode and tracks its subsynchronous frequency accurately. The ASDC utilizes voltage signals to extract the SSO at the estimated subsynchronous frequency, generates appropriate current signals, and injects them into the grid through a shunt-voltage sourced converter (SVSC). The ASDC behaves like a variable impedance, which essentially reshapes the system's impedance response by adding a positive resistance at the estimated subsynchronous frequency. The parameters of the phase-shifters are computed using Brent's root-finding method such that the desired phase-shift at the estimated subsynchronous frequency is always maintained. The control's adaptiveness and damping performance under different operating scenarios is verified through extensive EMT simulations on a real-world wind power system facing SSO using PSCAD.

Data-driven model-free adaptive damping control with unknown control direction for wind farms

The wide-area damping control (WADC) designed by conventional model-based methods faces the challenge from the strong nonlinearity, time-varying structure, and disturbances from complex operating conditions of the interconnected multi-machine power systems. In this paper, as one of the data-driven methods, the model-free adaptive control (MFAC), which employs the pseudo-partial derivative (PPD) to linearize the nonlinear power system dynamically, is adopted to design WADC for its advantages of the low cost, simple structure & low calculation burden, and the analyzable stability. However, when applied to the WADC design, the prototype MFAC is infeasible due to the assumed known control direction, the non-zero steady-state controller output, and unconsidered disturbances. To solve these problems, a novel data-driven adaptive WADC, consisting of control direction correction, PPD updating, one-step-ahead weighted predictive control, is proposed, moreover, the stability of the closed-loop system considering disturbances is thoroughly analyzed. Finally, case studies with various disturbances are conducted to validate the proposed control strategy.

Adaptive Damping Design of PMSG Integrated Power System with Virtual Synchronous Generator Control

With the continuous development of wind power capacity, a large number of wind turbines connected by power electronic devices make the system inertia lower, which leads to the problem of system frequency stability degradation. The virtual synchronous generator (VSG) control can make wind turbines possess inertia and damping. However, the stochastic dynamic behavior of wind generation results in the stochastic changing of operating condition; this paper presents an adaptive subsynchronous oscillation (SSO) damping control method for the wind generation with VSG control. Firstly, the small signal model of the permanent magnet synchronous generator (PMSG) with VSG is built, and the model of state space is derived and built. The active power of PMSG is selected as the variable parameter vector to establish a polytopic linear variable parameter system model. Then, based on the hybrid H2/H∞ control method, each vertex state feedback matrix is solved by linear matrix inequality, and a subsynchronous oscillation adaptive damping controller with polytope is obtained. Finally, the 4-machine 2-area system connected to two PMSGs with VSG control is used as the test system for time domain simulation. The simulation results demonstrate that the LPV based adaptive damping controller could provide enough damping under the circumstances of wider changes of wind power outputs.

Adaptive Damping Control Strategy of Wind Integrated Power System

Random variation of grid-connected wind power can cause stochastic variation of the power system operating point. This paper proposes a new scheme to design an adaptive damping controller by tracking the variation of system operating points and updating the controller’s functions to achieve a robust damping control effect. Firstly, the operating space is classified into different modes according to the classification of wind power outputs. Multiple power system stabilizers (PSSs) are then designed. Secondly, the method of optimal classification and regression decision tree (CART) is utilized for classifying subspaces of system operating point and it is proposed that the on-line measurements from wide area measurement system (WAMS) are used for tracking the dynamic behaviors of stochastic drifting point and thus guide the updating of appropriate PSSs be switched on adaptively. A 16-generator-68-bus power system integrated with wind power is presented as a test system to demonstrate that the adaptive control scheme by use of the CART can damp multi-mode oscillations effectively when the wind power output changes.

Adaptive Supplementary Damping Control of VSC-HVDC for Interarea Oscillation Using GrHDP

Interarea oscillation is one of the main challenges for the secure and stable operation of a large-scale interconnected power system. To address this problem, this paper presents an adaptive supplementary damping controller (SDC) for voltage source converter based high voltage direct current (VSC-HVDC) by using the goal representation heuristic dynamic programming (GrHDP). Compared with conventional heuristic dynamic programming (HDP), GrHDP adds a new goal representation network to generate an adaptive reward signal to achieve better mapping relationship between the system states and the control action. To compensate phase shift between the output and input signals of a large-scale interconnected AC/DC power system, this paper proposes a parallel phase shift channel for the GrHDP. Moreover, the proposed GrHDP-SDC is a model-free control scheme, which does not need the mathematic model of the power system and has quick online learning ability to adapt to the variation of system operating conditions. Case studies are carried out on the 10-machine 39-bus system and 16-machine 68-bus system, respectively. Compared with the conventional lead-lag SDC (LL-SDC) and HDP-SDC, simulation results show that the proposed GrHDP-SDC can always provide satisfactory damping performance over a wide range of system operating conditions and disturbances.

Adaptive controller design based on input-output signal selection for voltage source converter high voltage direct current systems to improve power system stability

An input-output signal selection based on Phillips-Heffron model of a parallel high voltage alternative current/high voltage direct current (HVAC/HVDC) power system is presented to study power system stability. It is well known that appropriate coupling of inputs-outputs signals in the multivariable HVDC-HVAC system can improve the performance of designed supplemetary controller. In this work, different analysis techniques are used to measure controllability and observability of electromechanical oscillation mode. Also inputs–outputs interactions are considered and suggestions are drawn to select the best signal pair through the system inputs-outputs. In addition, a supplementary online adaptive controller for nonlinear HVDC to damp low frequency oscillations in a weakly connected system is proposed. The results obtained using MATLAB software show that the best output-input for damping controller design is rotor speed deviation as out put and phase angle of rectifier as in put. Also response of system equipped with adaptive damping controller based on HVDC system has appropriate performance when it is faced with faults and disturbance.

Novel Adaptive Neural Controller Design Based on HVDC Transmission System to Damp Low Frequency Oscillations and Sub Synchronous Resonance

This paper presents the effect of the high voltage direct current (HVDC) transmission system based on voltage source converter (VSC) on the sub synchronous resonance (SSR) and low frequency oscillations (LFO) in power system. Also, a novel adaptive neural controller based on neural identifier is proposed for the HVDC which is capable of damping out LFO and sub synchronous oscillations (SSO). For comparison purposes, results of system based damping neural controller are compared with a lead-lag controller based on quantum particle swarm optimization (QPSO). It is shown that implementing adaptive damping controller not only improves the stability of power system but also can overcome drawbacks of conventional compensators with fixed parameters. In order to determine the most effective input of HVDC system to apply supplementary controller signal, analysis based on singular value decomposition is performed. To evaluate the performance of the proposed controller, transient simulations of detailed nonlinear system are considered.

Optimal IPFC damping controller design based on simplex method and self-tuned fuzzy damping scheme in a two-area multimachine power system

This paper develops a novel self-tuned fuzzy damping control scheme for an interline power flow controller (IPFC) to suppress the interarea mode of oscillations in a multimachine power system. The nonlinear adaptive damping controller is based on coordinated operation of two fuzzy inference systems. The first one produces the required $q$-axis voltage reference of the quasi multipulse series converter in response to generator angle oscillations, while the second one is used to tune the output of the first one online for further reducing the error signal using a given set of fuzzy rules. The simplex method is employed to search for optimal gains of the damping controller by minimizing the objective function in which speed deviations between generators are formulated. The feasibility of the proposed technique is validated using time-domain simulation cases in the PSCAD simulation program. It is also shown that the proposed damping scheme for IPFC works better than the static synchronous series compensator, which utilizes the same damping scheme in reducing the interarea mode of oscillations.

Adaptive Damping Control of Inter-Area Oscillations Based on Federated Kalman Filter Using Wide Area Signals

The application of phasor measurement unit (PMU) measurements and adaptive wide-area damping control based on federated Kalman filters are investigated. The Kalman filters used for estimating the operating conditions are federated using chi-square variables and a confidence interval. When the power system is operating precisely at a preselected operating point, only the corresponding controller is active. When the power system is not exactly at the operating point, a combined controller is formed. A 68-bus, 16-generator system is used as a test system. Three DC lines, three ESDs, and remote PMU frequencies as added inputs are used as the example control. The simulation results demonstrated the good performances for adaptive damping the inter-area oscillation modes.

TCSC Nonlinear Adaptive Damping Controller Design Based on RBF Neural Network to Enhance Power System Stability

In this paper, a nonlinear adaptive damping controller based on radial basis function neural network (RBFNN), which can infinitely approximate to nonlinear system, is proposed for thyristor controlled series capacitor (TCSC). The proposed TCSC adaptive damping controller can not only have the characteristics of the conventional PID, but adjust the parameters of PID controller online using identified Jacobian information from RBFNN. Hence, it has strong adaptability to the variation of the system operating condition. The effectiveness of the proposed controller is tested on a two-machine five-bus power system and a four-machine two-area power system under different operating conditions in comparison with the lead-lag damping controller tuned by evolutionary algorithm (EA). Simulation results show that the proposed damping controller achieves good robust performance for damping the low frequency oscillations under different operating conditions and is superior to the lead-lag damping controller tuned by EA.

Design of model predictive controllers for adaptive damping of inter-area oscillations

An adaptive damping controller design method by integrating online recursive closed-loop subspace model identification (SMI) with model predictive control theory is proposed in this paper. A reduced order state-space model which contains dominant low frequency oscillation modes is firstly identified online by using an closed-loop SMI algorithm. Then, an infinite horizon closed-loop optimal control is achieved based on model prediction which uses the current state of power system as the initial state. At each control step, the identified model is updated by using the new coming measurements and the optimal control action is solved again. Periodical online model and control updating identification and optimal control enables the proposed controller to adapt to operating condition variations and system parameter uncertainties. It is more robust than offline identification based damping controllers which could suffer from performance degradation under time varying and uncertain conditions. Simulation results demonstrate the effectiveness and robustness of the proposed controller in damping inter-area low frequency oscillations. The abilities to coordinate with power system stabilizers (PSSs) and other similar controllers in multi-machine power systems are also illustrated.

A heuristic training-based least squares support vector machines for power system stabilization by SMES

This paper presents the application of least squares support vector machines (LS-SVMs) to design of an adaptive damping controller for superconducting magnetic energy storage (SMES). To accelerate LS-SVMs training and testing, a large amount of training data set of a multi-machine power system is reduced by the measurement of similarity among samples. In addition, the redundant data in the training set can be significantly discarded. The LS-SVM for SMES controllers are trained using the optimal LS-SVM parameters optimized by a particle swarm optimization and the reduced data. The LS-SVM control signals can be adapted by various operating conditions and different disturbances. Simulation results in a two-area four-machine power system demonstrate that the proposed LS-SVM for SMES controller is robust to various disturbances under a wide range of operating conditions in comparison to the conventional SMES.

An adaptive neural damping controller for HVDC transmission systems

This paper presents a novel linearized model of a power system installed HVAC parallel-connected with a HVDC system based voltage source converter (VSC HVDC) in order to determine the most effective input to apply supplementary controller and design of phase compensator. The power system linearized model cannot be appropriate during the severe disturbances such as three phase faults. In this paper, a nonlinear model of a power system is obtained and in addition, an adaptive neural damping controller based neural identifier is proposed to improve stability and overcome the drawbacks of conventional phase compensator. Simulation results carried out by SIMULINK/MATLAB show the proposed strategy is most effective in comparison with phase compensator. Copyright © 2010 John Wiley & Sons, Ltd.

06/00669 Accelerating residential PV expansion: demand analysis for competitive electricity markets: Duke, R. et al. Energy Policy, 2005, 33, (15), 1912–1929.

This paper presents the application of least squares support vector machines (LS-SVMs) to design of an adaptive damping controller for superconducting magnetic energy storage (SMES). To accelerate LS-SVMs training and testing, a large amount of training data set of a multi-machine power system is reduced by the measurement of similarity among samples. In addition, the redundant data in the training set can be significantly discarded. The LS-SVM for SMES controllers are trained using the optimal LS-SVM parameters optimized by a particle swarm optimization and the reduced data. The LS-SVM control signals can be adapted by various operating conditions and different disturbances. Simulation results in a two-area four-machine power system demonstrate that the proposed LS-SVM for SMES controller is robust to various disturbances under a wide range of operating conditions in comparison to the conventional SMES.

Enhancement of power system dynamic performance through an on-line self-tuning adaptive SVC controller

Static VAr compensators (SVC) are used for voltage control of long distance bulk power transmission lines. By using a supplemental control loop an SVC can also be used to improve the dynamic and transient stability of a power system. Use of a self-tuning adaptive control algorithm as a supplementary controller for the SVC is presented in this article. The control derived is based on a pole-shifting technique employing a predicted plant model. Simulation studies on a simple power system model showed rapid convergence of the estimated plant parameters with an extremely good damping profile. The controller has been tested for ranges of operating conditions and for various disturbances. The effectiveness of the adaptive damping controller was also evaluated through an ‘optimized’ PI controller.