Jin Dong

Application of distributed control to mitigate disturbance propagations in large power networks

During the past decades the electric power infrastructure has evolved into one of the largest and most complex systems due to its extreme dimension, geographic reach and high reliability requirements. Maintaining sufficient security margins requires major enhancement of the existing control. Particular emphasis should be placed on improving the ability of the system to survive extreme contingencies, triggered by very unlikely chains of events, but capable of propagating into widespread outages. In this paper, a distributed control scheme is proposed to mitigate disturbance propagations in large power networks. We find a linear state feedback that simultaneously optimizes a standard Linear Quadratic Regulator (LQR) cost criterion and induces a pre-defined communication structure. The proposed controller provides supplementary damping through the excitation of the generators. The main advantage of this approach lies in the limited communication and limited model information required for the design which makes it practically applicable for large scale systems. We use a large two-dimensional mesh structure test system with homogenous parameters, to demonstrate that the proposed controller performs almost as well as the optimal centralized control with far less amount of communication and computation. The choice of test system is due to the fact that electromechanical wave propagation behavior observed in actual power systems can be readily recognized in that structure.

Frequency Prediction of Power Systems in FNET Based on State-Space Approach and Uncertain Basis Functions

In this paper, we discuss the modeling and prediction of power frequency. Power frequency is one of the most essential parameters in the monitoring, control, and protection of power systems and electric equipments because when a significant disturbance occurs in a power system, the frequency varies in time and space. It is critical to employ a dependable model in order to optimize the efficiency and reliability of power systems in the Frequency Monitoring Network (FNET ), and thus, prevent frequency oscillation in power grid. This paper describes the use of a state-space model and basis functions to predict power frequency. In the state-space method, expectation maximization (EM) and prediction error minimization (PEM) algorithms are used to dynamically estimate the models parameters. In the basis functions method, we employ random basis functions to predict the frequency. The algorithms are easy to implement online, having both high precision and a short response time. Numerical results are presented to demonstrate that the proposed techniques are able to achieve good performance in frequency prediction.

Real-time prediction of power system frequency in FNET: A state space approach

This paper proposes a novel approach to predict power frequency by applying a state-space model to describe the time-varying nature of power systems. It introduces the Expectation maximization (EM) and prediction error minimization (PEM) algorithms to dynamically estimate the parameters of the model. In this paper, we discuss how the proposed models can be used to ensure the efficiency and reliability of power systems in Frequency Monitoring Network (FNET), if serious frequency fluctuation or measurement failure occur at some nodes; this is achieved without requiring the exact model of complex power systems. Our approach leads to an easy online implementation with high precision and short response time that are key to effective frequency control. We randomly pick a set of frequency data for one power station in FNET and use it to estimate and predict the power frequency based on past measurements. Several computer simulations are provided to evaluate the method. Numerical results showed that the proposed technique could achieve good performance regarding the frequency monitoring with very limited measurement input information.

Reduced order modeling for fluid flows subject to quadratic type nonlinearities

Explicit model reduction for nonlinear systems with no prior information about the type of nonlinearity involved is difficult and challenging. It is easier to reduce nonlinear systems which nonlinearity is known. In this paper we introduce two nonlinear model reduction techniques for quadratic nonlinear systems. The first technique is nonlinear balanced truncation. The Controlability and observability gramians are computed by solving the Hamilton Jacobi equations and then used to find the transformation function to get the nonlinear balanced truncated system. The second technique is using Arnoldi algorithm. We apply both techniques to a practical nonlinear quadratic system which is the two-dimensional Burgers equation problem of a fluid passing an obstacle.

PAPMSC: Power-Aware Performance Management Approach for Virtualized Web Servers via Stochastic Control

As green computing is becoming a popular computing paradigm, the performance of energy-efficient data center becomes increasingly important. This paper proposes power-aware performance management via stochastic control method (PAPMSC), a novel stochastic control approach for virtualized web servers. It addresses the instability and inefficiency issues due to dynamic web workloads. It features a coordinated control architecture that optimizes the resource allocation and minimizes the overall power consumption while guaranteeing the service level agreements (SLAs). More specifically, due to the interference effect among the co-located virtualized web servers and time-varying workloads, the relationship between the hardware resource assignment to different virtual servers and the web applications’ performance is considered as a coupled Multi-Input-Multi-Output (MIMO) system and formulated as a robust optimization problem. We propose a constrained stochastic linear-quadratic controller (cSLQC) to solve the problem by minimizing the quadratic cost function subject to constraints on resource allocation and applications’ performance. Furthermore, a proportional controller is integrated to enhance system stability. In the second layer, we dynamically manipulate the physical frequency for power efficiency using an adaptive linear quadratic regulator (ALQR). Experiments on our testbed server with a variety of workload patterns demonstrate that the proposed control solution significantly outperforms existing solutions in terms of effectiveness and robustness.

Duality of the optimal distributed control for spatially invariant systems

We consider the problem of optimal distributed control of spatially invariant systems. We develop an input-output framework for problems of this class. Spatially invariant systems are viewed as multiplication operators from a particular Hilbert function space into itself in the Fourier domain. Optimal distributed performance is then posed as a distance minimization in a general L-infinity space from a vector function to a subspace with a mixed L∞ and H∞ space structure. In this framework, a generalized version of the Youla parametrization plays a central role. The duality structure of the problem is characterized by computing various dual and pre-dual spaces. The annihilator and pre-annihilator subspaces are also calculated for the dual and pre-dual problems. Furthermore, the latter is used to show the existence of optimal distributed controllers and dual extremal functions under certain conditions. The dual and pre-dual formulations lead to finite dimensional convex optimizations which approximate the optimal solution within desired accuracy. These optimizations can be solved using convex programming methods. Our approach is purely input-output and does not use any state space realization.

Application of optimal production control theory for home energy management in a micro grid

We consider the optimal stochastic control problem for home energy systems with solar and energy storage devices when the demand is realized from the grid. The demand is subject to Brownian motions with both drift and variance parameters modulated by a continuous-time Markov chain that represents the regime of electricity price. We model the systems as pure stochastic differential equation models, and then we follow the completing square technique to solve the stochastic home energy management problem. The effectiveness of the efficiency of the proposed approach is validated through a simulation example. For practical situations with constraints consistent to those studied here, our results imply the proposed framework could reduce the electricity cost from short-term purchase in peak hour market.

Operator theoretic approach to the optimal distributed control problem for spatially invariant systems

This paper considers the problem of optimal distributed control of spatially invariant systems. The Banach space duality structure of the problem is characterized in terms of tensor product spaces. This complements the prior study undertaken by the authors, where the dual and pre-dual formulations were in terms of abstract spaces. Here, we show that these spaces together with the pre-annihilator and annihilator subspaces can be realized explicitly as specific tensor spaces and subspaces, respectively. The tensor space formulation leads to a solution in terms of an operator given by a tensor product. Specifically, the optimal distributed control performance for spatially invariant systems is equal to the operator induced norm of this operator. The results obtained in this paper bridge the gap between control theory and the metric theory of tensor product spaces.

Finite energy and bounded attacks on control system sensor signals

Control system networks are increasingly being connected to enterprise level networks. These connections leave critical industrial controls systems vulnerable to cyber-attacks. Most of the effort in protecting these cyber-physical systems (CPS) from attacks has been in securing the networks using information security techniques. Effort has also been applied to increasing the protection and reliability of the control system against random hardware and software failures. However, the inability of information security techniques to protect against all intrusions means that the control system must be resilient to various signal attacks for which new analysis methods need to be developed. In this paper, sensor signal attacks are analyzed for observer-based controlled systems. The threat surface for sensor signal attacks is subdivided into denial of service, finite energy, and bounded attacks. In particular, the error signals between states of attack free systems and systems subject to these attacks are quantified. Optimal sensor and actuator signal attacks for the finite and infinite horizon linear quadratic (LQ) control in terms of maximizing the corresponding cost functions are computed. The closed-loop systems under optimal signal attacks are provided. Finally, an illustrative numerical example using a power generation network is provided together with distributed LQ controllers.

Very short-term photovoltaic power forecasting using uncertain basis function

Solar photovoltaics (PV), one of the most promising and rapidly developing renewable energy technologies, has evolved towards becoming a main renewable electricity source. It is termed variable energy resources since solar irradiance is intermittent in nature. This variability is a critical factor when predicting the available energy of solar sources. Capital and operational costs associated with solar PV implementation are highly affected when inaccurate predictions are carried out. This paper presents a new forecasting model for solar PV by utilizing historical inter-minute data to outline a short-term probabilistic model of solar. The proposed methodology employs a probabilistic approach to predict short-term solar PV power based on uncertain basis functions. The PV forecasting model is applied to power generation from a 13.5 kW rooftop PV panel installed on the Distributed Energy, Communications, and Controls (DECC) laboratory at Oak Ridge National Laboratory. The results are compared with standard time series approach, which have shown a substantial improvement in the prediction accuracy of the total solar energy produced.