Pulse Compression Probing for Tracking Distribution Feeder Models

A Pulse-Compression Probing (PCP) method is applied in time-domain to identify an equivalent circuit model of a distribution network as seen from the transmission grid. A Pseudo-Random Binary Pulse Train (PRBPT) is injected as a voltage signal at the input of the feeder and processed to recover the impulse response. A transfer function and circuit model is fitted to the response, allowing the feeder to be modeled as a quasi-steady-state sinusoidal (QSSS) source behind a network. The method is verified on the IEEE 13-Node Distribution Test System, identifying a second order circuit model with less than seven cycles latency and a signal to noise ratio of 15.07 dB in the input feeder current.Comment: 5 Pages, 6 Figures, Pending Publication at IEEE PESGM 202

Weather Forecasting Error in Solar Energy Forecasting

As renewable distributed energy resources (DERs) penetrate the power grid at an accelerating speed, it is essential for operators to have accurate solar photovoltaic (PV) energy forecasting for efficient operations and planning. Generally, observed weather data are applied in the solar PV generation forecasting model while in practice the energy forecasting is based on forecasted weather data. In this paper, a study on the uncertainty in weather forecasting for the most commonly used weather variables is presented. The forecasted weather data for six days ahead is compared with the observed data and the results of analysis are quantified by statistical metrics. In addition, the most influential weather predictors in energy forecasting model are selected. The performance of historical and observed weather data errors is assessed using a solar PV generation forecasting model. Finally, a sensitivity test is performed to identify the influential weather variables whose accurate values can significantly improve the results of energy forecasting

Sensor - Assisted Enhancement of Cyber and Physical Resilience in Power Systems

The focus of this dissertation is on seeking ways to enhance cyber and physical resilience in power systems. The enhanced resilience is achieved via placement, networking, of modern digital sensors, and processing of their measurements. Here, cyber and physical resilience broadly refers to the ability to support uninterrupted system operation, and tolerance to random events and cyber-physical attacks. This dissertation details the development of a synchrophasor availability (SA)-constrained measurement network design to tolerate data loss/alteration, a sensor-defined power system partition algorithm to reduce computational and communication complexities in various monitoring tasks, and a transient stability assessment method to serve the system protection purpose in large lossy power systems. The SA-constrained sensor placement algorithm considers robust allocation of (additional) phasor measurement units (PMU) into a (new) PMU network in order to meet a data availability profile in the face of random communication interruptions, transmission line faults, and GPS spoofing attacks. SA at a bus is the fraction of time on average its time-synchronized current/voltage phasors are correctly present for real-time usage. The sensor-defined partition algorithm of a transmission network is applied to both real-time diagnosis of transmission circuit faults and to detection and isolation of GPS spoofed PMUs. The partitions are bordered by the measurement nodes of a PMU network. Conditions required for diagnosability and detectability are explicitly imposed on both the transmission and the measurement networks. The algorithm is applied to partitioning the IEEE 68-bus system, the IEEE 118-bus system, the Polish 3120-bus system and the PEGASE 9241-bus system. To address the challenges arising from stability assessment of large lossy power systems, a coverage-based stability assessment is pursued with focus on resolving computation and scalability issues for system protection purpose. This approach involves online tracking of each generator's electromechanical state using a local quasi-steady-state sinusoidal (QSSS) measurement model, and determining whether the state is enclosed in an offline-computed inner approximation of the post-fault region of attraction (RoA) at the time the RoA is established by a protection action. The approach to transient stability assessment is tested on a lossy 68-bus system subject to transmission faults

Fuzzy Predictive Control of a Boiler–Turbine System Based on a Hybrid Model System

This paper proposes a fuzzy predictive control scheme for controlling power output of a boiler–turbine system in the presence of disturbances and uncertainties. A new model of the boiler–turbine system is introduced based on the modeling approaches of hybrid systems, namely, the mixed logical dynamical modeling approach. Nonlinear parts of the system are linearized using the piecewise affine approach. To overcome the deficiency of the model predictive control in presence of disturbance and uncertainty, a fuzzy predictive control scheme is proposed in which a fuzzy supervisor is utilized to adjust the main predictive controller. The proposed fuzzy predictive control scheme has advantages such as simplicity and efficiency in nominal conditions and strong robustness in the presence of disturbances and uncertainties. Simulation results demonstrate the effectiveness and superiority of the method