Wide-area Control Of Power Systems Research Articles

Modified Q-Learning Method for Automatic Voltage Regulation in Wide-Area Multigeneration Systems

The state-estimation and optimal control of multigeneration systems are challenging for wide-area systems having numerous distributed automatic voltage regulators (AVR). This paper proposes a modified Q-learning method and algorithm that aim to improve the convergence of the approach and enhance the dynamic response and stability of the terminal voltage of multiple generators in the experimental Western System Coordinating Council (WSCC) and large-scale IEEE 39-bus test systems. The large-scale experimental testbed consists of a six-area, 39-bus system having ten generators that are connected to ten AVRs. The implementation shows promising results in providing stable terminal voltage profiles and other system parameters across a wide range of AVR systems under different test scenarios including N-1 contingency and fault conditions. The approach could provide significant stability improvement for wide-area systems as compared to the implementation of conventional methods such as using standalone AVR and/or power system stabilizers (PSS) for the wide-area control of power systems.

Adaptive Wide-Area Control of Power Systems Through Dynamic Load Modulating SVCs

This paper presents an adaptive wide-area control strategy based on energy function. The proposed method enhances the post-fault oscillation damping capability of Static Var Compensators (SVC) by incorporating load dynamics in the wide-area controller design that adapts to load variations. The load dynamics are obtained using Phasor Measurement Units (PMU) through load power and voltage data measurements, which are used to estimate a Moving Average (MA) dynamic load model. The obtained load model is then integrated into the wide-area controller to obtain the required SVC supplementary control action which modulates the load power in response to a disturbance based on inter-area dynamics obtained by employing a nonlinear Kalman filter and PMU measurements. The performance of the proposed strategy is evaluated on the simplified 14 generator model of the southeast Australian power system by using composite load models comprised of induction motor loads of various sizes and combinations. The obtained results show significant improvements in the system stability when the proposed control strategy is applied.

Dynamic state estimation of generators using spherical simplex unscented transform‐based unbiased minimum variance filter

Dynamic state estimation is essential in case of various monitoring, control, and protection strategies that are designed based on the state-space model. Kalman filter-based estimation algorithms are mainly used to estimate these states locally using the input and output measurements of the generator. However, in the case of wide-area power system control and protection strategies, remote estimation of these states is required. This remote estimation relies upon phasor measurement units for measurement signals, which are limited to output measurements such as voltage, current, and frequency. For Kalman filter-based techniques, apart from the output, input measurements such as field and torque input are also required to estimate the states. This study proposes an input invariant filter technique using unbiased minimum variance filter and spherical simplex unscented transform for remote estimation of generator states using the limited phasor measurement unit measurements. The estimation is performed in the absence of mechanical input torque and field voltage measurements using a minimum set of sigma points. The performance of the filter under various transient conditions and in the presence of measurement errors are analysed and compared with existing techniques.

Control Inversion: A Clustering-Based Method for Distributed Wide-Area Control of Power Systems

Wide-area control (WAC) has been shown to be an effective tool for damping low-frequency oscillations in power systems. In the current state of art, WAC is challenged by two main factors, namely, scalability of design and complexity of implementation. In this paper, we present a control design called control inversion that bypasses both of these challenges using the idea of clustering. The basic philosophy behind this method is to project the original power system model into a lower-dimensional state space through clustering and aggregation of generator states, and then designing a linear-quadratic regulator (LQR) controller for the lower-dimensional model. This controller is finally projected back to the original coordinates for wide-area implementation. The main problem is, therefore, posed as finding the projection which best matches the closed-loop performance of the WAC controller with that of a reference LQR controller for damping low-frequency oscillations. We verify the effectiveness of the proposed design using the IEEE 48-machine power system model of the Northeastern Power Coordinating Council.

Q-Learning-Based Damping Control of Wide-Area Power Systems Under Cyber Uncertainties

The installation of phasor measurement units brings about system-wide synchronized real-time measurements, which makes advanced closed-loop control of wide-area power systems (WAPSs) possible. In addition to uncertainties with the physical system, network imperfections are also critical for control performance and even stability of the system. This paper targets at wide-area damping control of WAPS under both physical and cyber uncertainties. The cyber uncertainties addressed in this paper include both communication delay and package dropout. First, a linearized WAPS model is developed with considerations of random control signal arrivals. Then, a ${\boldsymbol Q}$ -learning-based control algorithm is designed to learn the optimal control under both physical and cyber uncertainties. Finally, to further counteract the communication uncertainties between the control center and actuators, a novel control law is designed based on the statistical analysis to make the long-term control performance approach that under ideal communications. The designed control algorithm is tested with both simplified linear model and detailed nonlinear model of WAPSs. Simulation results demonstrate the effectiveness of the designed algorithm.

A delay-aware cyber-physical architecture for wide-area control of power systems

In this paper we address the problem of wide-area control of power systems using Synchrophasor measurements in the presence of network delays. We propose a novel cyber-physical architecture that uses an arbitrated network control systems approach for mitigating the destabilizing effects of delays in power systems. The approach consists of: (1) utilization of Synchrophasor measurements from distributed measurements across different buses in the power network, (2) estimation of delays that control messages experience, (3) a delay-aware control design that explicitly accommodates the delays and judiciously utilizes estimated system states when needed, and (4) a switching control strategy that aborts the computation of control signals when delays exceed a certain threshold to improve resource utilization. While the control gains are determined using a centralized power system model and state feedback, it is shown that the delay-aware aspects of the proposed architecture allow both distributed measurements and distributed implementation of the control law. The results are illustrated using a 50-bus, 14-generator, 4-area power system model. The results clearly demonstrate that the proposed controller recovers the ideal system performance (such as deviations in frequency<3mHz) even in the presence of large intra-area and inter-area delays with a small amount of additional control effort. Using the proposed overrun strategy, the results also confirm that about 30% drops can be accommodated with the proposed arbitrated network control systems approach.

Controller architectures: Tradeoffs between performance and structure

This review article describes the design of static controllers that achieve an optimal tradeoff between closed-loop performance and controller structure. Our methodology consists of two steps. First, we identify controller structure by incorporating regularization functions into the optimal control problem and, second, we optimize the controller over the identified structure. For large-scale networks of dynamical systems, the desired structural property is captured by limited information exchange between physical and controller layers and the regularization term penalizes the number of communication links. Although structured optimal control problems are, in general, nonconvex, we identify classes of convex problems that arise in the design of symmetric systems, undirected consensus and synchronization networks, optimal selection of sensors and actuators, and decentralized control of positive systems. Examples of consensus networks, drug therapy design, sensor selection in flexible wing aircrafts, and optimal wide-area control of power systems are provided to demonstrate the effectiveness of the framework.

Genetic algorithm‐based phasor measurement unit placement method considering observability and security criteria

The phasor measurement technology has made it possible the monitoring and control of wide-area power systems. In this study, a new genetic algorithm based method for optimal placement of phasor measurement units (PMUs) considering observability and security issues is proposed. The idea is to allocate the least number of PMUs while providing the most redundant set of measurements. This allocation philosophy ensures reliability in the state estimation process. Moreover, the allocation method also takes into account security issues, by preserving the system's observability in case of loss of PMUs. In particular, the loss of a single PMU [(n − 1) criterion] is considered. The proposed method was tested on IEEE standard test systems and the results are discussed and evaluated.

Stabilizing control of wide-area power system with signal transmission delay based on Hamiltonian functional method

The stability of wide-area power system with signal transmission delay is investigated in this paper. A non-linear time-delay Hamiltonian model of power system is constructed. Based on the proposed model, the Hamiltonian functional method is used to derive a delay-dependent steady stability criterion in term of matrix inequalities by constructing suitable Lyapunov–Krasovskii functional. Then the wide-area damping controller (WADC) for the power system is designed based on the delay-dependent sufficient conditions. The constant time-delay introduced by the transmission of the remote signals is investigated. The relationship between the parameters of WADC and the delay margin has been investigated. It is used to guide the design of WADC and achieve a trade-off between the damping performance and delay margin. Four-generator eleven-bus power system is used to illustrate delay effect on inter-area mode damping. The performance of the proposed controller is verified by the results of simulation in time-domain, and it is proved that the method proposed in this paper is effective.

Hierarchical Wide-Area Control of Power Systems Including Wind Farms and FACTS for Short-Term Frequency Regulation

In this work a hierarchical scheme is proposed for the coordinated control of conventional synchronous generators, wind farm converters and flexible ac transmission systems. It comprises two levels, namely a fully decentralized set of controllers associated with the involved devices and a centralized coordinating controller based on synchronized wide-area signals provided by phasor measurement units. The proposed hierarchy of controllers is mainly focused on two objectives: transient frequency support and inter-area oscillations damping. Several works have recently proved that the fast-acting power converters of variable-speed generators can greatly improve the short-term frequency regulation capability in scenarios with a large penetration of wind energy. A case study is included showing that the dynamic performance and stability of power systems can indeed be enhanced when windmill converters are properly coordinated via a wide-area centralized controller.

Wide-Area Control of Power Systems Through Delayed Network Communication

Like general network communication, there are network-induced delays, data packet dropout and disordering in the communication of wide-area measurement systems. What impact do these factors have on the control of wide-area closed-loop power systems? This study aims at developing methods in order to take these factors into account in control of wide-area power systems. First, a networked control system model is constructed for wide-area closed-loop power systems; in this model, network-induced delays, data packet dropout, and disordering are captured by time-varying delays in wide-area measurement systems. Then, linear matrix inequality based methods are applied to design a controller for better power system performance using wide-area information as feedback signals. The controller can tolerate network-induced delays, data packet dropout, and disordering in the communication of wide-area measurement systems. Finally, we give some simulation results showing the effectiveness of our approach.

Latency Viewed as a Stochastic Process and its Impact on Wide Area Power System Control Signals

A method of calculating the communication delay (latency) for measurements and control signals in a power system is shown. The basis of the calculation is a dedicated communication channel for control signals. The time delay calculation is examined using a dynamic equivalent of the Western Electricity Coordinating Council transmission system. The impact on control system response is discussed. The application given is a wide area control system for interarea mode damping. Results demonstrate that control signal latency can degrade the performance of controls in a wide area control system.