skip to main content

Title: Distributed Model Predictive Control for Autonomous Droop-Controlled Inverter-Based Microgrids
Microgrids must be able to restore voltage and frequency to their reference values during transient events; inverters are used as part of a microgrid's hierarchical control for maintaining power quality. Reviewed methods either do not allow for intuitive trade-off tuning between the objectives of synchronous state restoration, local reference tracking, and disturbance rejection, or do not consider all of these objectives. In this paper, we address all of these objectives for voltage restoration in droop-controlled inverter-based islanded micro-grids. By using distributed model predictive control (DMPC) in series with an unscented Kalman Filter (UKF), we design a secondary voltage controller to restore the voltage to the reference in finite time. The DMPC solves a reference tracking problem while rejecting reactive power disturbances in a noisy system. The method we present accounts for non-zero mean disturbances by design of a random-walk estimator. We validate the method's ability to restore the voltage in finite time via modeling a multi-node microgrid in Simulink.
Authors:
; ; ;
Award ID(s):
1743772
Publication Date:
NSF-PAR ID:
10190925
Journal Name:
2019 IEEE 58th Conference on Decision and Control (CDC)
Page Range or eLocation-ID:
6242 to 6248
Sponsoring Org:
National Science Foundation
More Like this
  1. This paper examines the problem of real-time optimization of networked systems and develops online algorithms that steer the system towards the optimal trajectory without explicit knowledge of the system model. The problem is modeled as a dynamic optimization problem with time-varying performance objectives and engineering constraints. The design of the algorithms leverages the online zero-order primal-dual projected-gradient method. In particular, the primal step that involves the gradient of the objective function (and hence requires a networked systems model) is replaced by its zero-order approximation with two function evaluations using a deterministic perturbation signal. The evaluations are performed using the measurements of the system output, hence giving rise to a feedback interconnection, with the optimization algorithm serving as a feedback controller. The paper provides some insights on the stability and tracking properties of this interconnection. Finally, the paper applies this methodology to a real-time optimal power flow problem in power systems, and shows its efficacy on the IEEE 37-node distribution test feeder for reference power tracking and voltage regulation.
  2. In this paper we design a hybrid predictive controller for the tracking of a sinusoidal reference signal. The stability and forward invariance of a set of points around the reference state, named the tracking ellipse, is established by using tools for hybrid dynamical systems. Moreover, prediction of solutions for a finite number of switching events is used to minimize the number of switches. The control algorithm is shown to be robust to small perturbations and input disturbances. Simulations illustrating the main results are included.
  3. This paper proposes a finite-time event-triggered secondary frequency and voltage control for islanded AC microgrids (MGs) in a distributed fashion. The proposed control strategy can effectively perform frequency restoration and voltage regulations, while sharing the active and reactive power among the distributed generators (DGs) based on their power ratings. The finite-time control enables a system to reach consensus in a finite period of time enhanced from the asymptotic convergence. The event-triggered communication is utilized to reduce the communication burden among the DG controllers by transmitting data among DGs if an event-triggering condition is satisfied. The performance of the proposed finite-time event-triggered frequency control is verified utilizing a hardware-in-the-loop experimental testbed which simulates an AC MG in Opal-RT.
  4. Abstract
    The applications for wide area monitoring, protection, and control systems (WAMPC) at the control center, help with providing resilient, efficient, and secure operation of the transmission system of the smart grid. The increased proliferation of phasor measurement units (PMUs) in this space has inspired many prudent applications to assist in the process of decision making in the control centers. Machine learning (ML) based decision support systems have become viable with the availability of abundant high-resolution wide area operational PMU data. We propose a deep neural network (DNN) based supervisory protection and event diagnosis system and demonstrate that it works with very high degree of confidence. The system introduces a supervisory layer that processes the data streams collected from PMUs and detects disturbances in the power systems that may have gone unnoticed by the local monitoring and protection system. Then, we investigate compromise of the insights of this ML based supervisory control by crafting adversaries that corrupt the PMU data via minimal coordinated manipulation and identification of the spatio-temporal regions in the multidimensional PMU data in a way that the DNN classifier makes wrong event predictions.
    Other
    This dataset contains images that represent PMU data described in the reference paper.More>>
  5. At high penetration level of photovoltaic (PV) generators, their abrupt disturbances (caused by moving clouds) cause voltage and frequency perturbations and increase system losses. Meanwhile, the daily irradiation profile increases the slope in the net-load profile, for example, California duck curve, which imposes the challenge of quickly bringing on-line conventional generators in the early evening hours. Accordingly, this paper presents an approach to achieve two objectives: (1) address abrupt disturbances caused by PV generators, and (2) shape the net load profile. The approach is based on employing battery energy storage (BES) systems coupled with PV generators and equipped with proper controls. The proposed BES addresses these two issues by realizing flexible power ramp-up and ramp-down rates by the combined PV and BES. This paper presents the principles, modeling and control design aspects of the proposed system. A hybrid dc/ac study system is simulated and the effectiveness of the proposed BES in reducing the impacts of disturbances on both the dc and ac subsystems is verified. It is then shown that the proposed PV-BES modifies the daily load profile to mitigate the required challenge for quickly bringing on-line synchronous generators.