This study describes the implementation of a tool to estimate latencies and data dropouts in communication networks transferring synchrophasor data defined by the C37.118 standard. The tool assigns a time tag to synchrophasor packets at the time it receives them according to a global positioning system clock and with this information is able to determine the time those packets took to reach the tool. The tool is able to connect simultaneously to multiple phasor measurement units (PMUs) sending packets at different reporting rates with different transport protocols such as user datagram protocol or transmission control protocol. The tool is capable of redistributing every packet it receives to a different device while recording the exact time this information is re-sent into the network. The results of measuring delays from a PMU using this tool are presented and compared with those of a conventional network analyzer. The results show that the tool presented in this paper measures delays more accurately and precisely than the conventional network analyzer.
This project explored coupling modeling and analysis methods from multiple domains to address complex hybrid (cyber and physical) attacks on mission critical infrastructure. Robust methods to integrate these complex systems are necessary to enable large trade-space exploration including dynamic and evolving cyber threats and mitigations. Reinforcement learning employing deep neural networks, as in the AlphaGo Zero solution, was used to identify "best" (or approximately optimal) resilience strategies for operation of a cyber/physical grid model. A prototype platform was developed and the machine learning (ML) algorithm was made to play itself in a game of 'Hurt the Grid'. This proof of concept shows that machine learning optimization can help us understand and control complex, multi-dimensional grid space. A simple, yet high-fidelity model proves that the data have spatial correlation which is necessary for any optimization or control. Our prototype analysis showed that the reinforcement learning successfully improved adversary and defender knowledge to manipulate the grid. When expanded to more representative models, this exact type of machine learning will inform grid operations and defense - supporting mitigation development to defend the grid from complex cyber attacks! This same research can be expanded to similar complex domains.
A wide-area controller to damp inter-area oscillations in the North American Western Interconnection (WI) by modulating power transfers in a HVDC link is used in this paper to investigate the effects that latencies in its feedback signals have on its performance. This controller uses two feedback measurements to perform its control action. The analysis show that the stabilizing effect of the controller in transient stability and small signal stability is compromised as the feedback measurements experience higher delays. The results show that one of the feedback signals can tolerate more delay than the other. The analysis was performed with Bode plots and time domain simulations on a reduced order model of the WI from which a linear version was obtained.
A wide-area controller to damp inter-area oscillations in the North American Western Interconnection (WI) by modulating power transfers in a HVDC link is used in this paper to investigate the effects that latencies in its feedback signals have on its performance. This controller uses two feedback measurements to perform its control action. The analysis show that the stabilizing effect of the controller in transient stability and small signal stability is compromised as the feedback measurements experience higher delays. The results show that one of the feedback signals can tolerate more delay than the other. The analysis was performed with Bode plots and time domain simulations on a reduced order model of the WI from which a linear version was obtained.
This paper discusses the optimal output feedback control problem of linear time-invariant systems with additional restrictions on the structure of the optimal feedback control gain. These restrictions include setting individual elements of the optimal gain matrix to zero and making the sum of certain rows of the gain matrix equal to desired values. The paper proposes a method that modifies the standard quadratic cost function to include soft constraints ensuring the satisfaction of these restrictions on the structure of the optimal gain. Necessary conditions for optimality with these soft constraints are derived, and an algorithm to solve the resulting optimal output feedback control problem is given. Finally, a power systems example is presented to illustrate the usefulness of proposed approach.
This paper discusses the broad use of rotational kinetic energy stored in wind turbine rotors to supply services to the electrical power grid. The grid services are discussed in terms of zero-net-energy, which do not require a reduction in power output via pitch control (spill), but neither do they preclude doing so. The services discussed include zero-net-energy regulation, transient and small signal stability, and other frequency management services. The delivery of this energy requires a trade-off between the frequency and amplitude of power modulation and is limited, in some cases, by equipment ratings and the unresearched long-term mechanical effects on the turbine. As wind displaces synchronous generation, the grid's inertial storage is being reduced, but the amount of accessible kinetic energy in a wind turbine at rated speed is approximately 6 times greater than that of a generator with only a 0.12% loss in efficiency and 75 times greater at 10% loss. The potential flexibility of the wind's kinetic storage is also high. However, the true cost of providing grid services using wind turbines, which includes a potential increase in operations and maintenance costs, have not been compared to the value of the services themselves.
This report documents the use of wind turbine inertial energy for the supply of two specific electric power grid services; system balancing and real power modulation to improve grid stability. Each service is developed to require zero net energy consumption. Grid stability was accomplished by modulating the real power output of the wind turbine at a frequency and phase associated with wide-area modes. System balancing was conducted using a grid frequency signal that was high-pass filtered to ensure zero net energy. Both services used Phasor Measurement Units (PMUs) as their primary source of system data in a feedforward control (for system balancing) and feedback control (for system stability).
This paper presents simulation results of a control scheme for damping inter-area oscillations using high-voltage DC (HVDC) power modulation. The control system utilizes realtime synchrophasor feedback to construct a supplemental commanded power signal for the Pacific DC Intertie (PDCI) in the North American Western Interconnection (WI). A prototype of this controller has been implemented in hardware and, after multiple years of development, successfully tested in both open and closed-loop operation. This paper presents simulation results of the WI during multiple severe contingencies with the damping controller in both open and closed-loop. The primary results are that the controller adds significant damping to the controllable modes of the WI and that it does not adversely affect the system response in any of the simulated cases. Furthermore, the simulations show that a feedback signal composed of the frequency difference between points of measurement near the Washington-Oregon border and the California-Oregon border can be employed with similar results to a feedback signal constructed from measurements taken near the Washington-Oregon border and southern California. This is an important consideration because it allowed the control system to be designed without relying upon cross-system measurements, which would have introduced significant additional delay.
Distributed control compensation based on local and remote sensor feedback can improve small-signal stability in large distributed systems, such as electric power systems. Long distance remote measurements, however, are potentially subject to relatively long and uncertain network latencies. In this work, the issue of asymmetrical network latencies is considered for an active damping application in a two-area electric power system. The combined effects of latency and gain are evaluated in time domain simulation and in analysis using root-locus and the maximum singular value of the input sensitivity function. The results aid in quantifying the effects of network latencies and gain on system stability and disturbance rejection.
Distributed control compensation based on local and remote sensor feedback can improve small-signal stability in large distributed systems, such as electric power systems. Long distance remote measurements, however, are potentially subject to relatively long and uncertain network latencies. In this work, the issue of asymmetrical network latencies is considered for an active damping application in a two-area electric power system. The combined effects of latency and gain are evaluated in time domain simulation and in analysis using root-locus and the maximum singular value of the input sensitivity function. The results aid in quantifying the effects of network latencies and gain on system stability and disturbance rejection.