Reza Arghandeh

Micro-synchrophasors for distribution systems

This paper describes a research project to develop a network of high-precision phasor measurement units, termed micro-synchrophasors or μPMUs, and explore the applications of μPMU data for electric power distribution systems.

Flywheel Energy Storage Systems for Ride-through Applications in a Facility Microgrid

Flywheel energy storage (FES) has attracted new interest for uninterruptible power supply (UPS) applications in a facility microgrid. Due to technological advancements, the FES has become a promising alternative to traditional battery storage technologies. This paper aims at developing a tool to demonstrate the use of FES units for securing critical loads during a utility outage in a microgrid environment. The FES is modeled, simulated and evaluated in the MATLAB/SIMULINK® environment. A data center is used to represent a facility microgrid case study. It illustrates how an FES can help improve the load serving capability and provide a highly reliable ride-through capability for critical loads during a utility disturbance. In comparison with batteries, the application of FES for power security is new on the horizon. This limits the availability of experimental data. The simulation model presented in this paper will enable the analysis of short-term ride-through applications of FES during an islanded operation of a facility microgrid. As a result, it can provide a guideline for facility engineers in a data center or other types of facility microgrids to better design their backup power systems based on FES technology, which can be used in combination with traditional fuel-based generators.

Bayesian linear state estimation using smart meters and PMUs measurements in distribution grids

In this work we address the problem of static state estimation (SE) in distribution grids by leveraging historical meter data (pseudo-measurements) with real-time measurements from synchrophasors (PMU data). We present a Bayesian linear estimator based on a linear approximation of the power flow equations for distribution networks, which is computationally more efficient than standard nonlinear weighted least squares (WLS) estimators. We show via numerical simulations that the proposed strategy performs similarly to the standard WLS estimator on a small distribution network. A key advantage of the proposed approach is that it provides explicit off-line computation of the estimation error confidence intervals, which we use to explore the tradeoffs between number of PMUs, PMU placement and measurement uncertainty. Since the estimation error in distribution systems tends to be dominated by uncertainty in loads and scarcity of instrumented nodes, the linearized method along with the use of high-precision PMUs may be a suitable way to facilitate on-line state estimation where it was previously impractical.

Data-driven approach for distribution network topology detection

This paper proposes a data-driven approach to detect the switching actions and topology transitions in distribution networks. It is based on the real time analysis of time-series voltages measurements. The analysis approach draws on data from high-precision phasor measurement units (μPMUs or synchrophasors) for distribution networks. The key fact is that time-series measurement data taken from the distribution network has specific patterns representing state transitions such as topology changes. The proposed algorithm is based on comparison of actual voltage measurements with a library of signatures derived from the possible topologies simulation. The IEEE 33-bus model is used for the algorithm validation.

Topology detection in microgrids with micro-synchrophasors

Network topology in distribution networks is often unknown, because most switches are not equipped with measurement devices and communication links. However, knowledge about the actual topology is critical for safe and reliable grid operation. This paper proposes a voting-based topology detection method based on micro-synchrophasor measurements. The minimal difference between measured and calculated voltage angle or voltage magnitude, respectively, indicates the actual topology. Micro-synchrophasors or micro-Phasor Measurement Units (μPMU) are high-precision devices that can measure voltage angle differences on the order of ten millidegrees. This accuracy is important for distribution networks due to the smaller angle differences as compared to transmission networks. For this paper, a microgrid test bed is implemented in MATLAB with simulated measurements from μPMUs as well as SCADA measurement devices. The results show that topologies can be detected with high accuracy. Additionally, topology detection by voltage angle shows better results than detection by voltage magnitude.

The Local Team: Leveraging Distributed Resources to Improve Resilience

In recent years, extreme weather events have severely affected the performance of the electric grid. Very large-scale events (VLSE) with potentially catastrophic impacts on the grid pose more than an inconvenience in todays electricity-driven lifestyle, and the frequency and severity of such events may continue to increase as a consequence of global climate change. This article summarizes the state of the art in leveraging distributed resources to improve resilience of the electric grid. It also highlights the technical questions that need to be addressed through additional research and development if the value of distributed resources is to be maximized.

Smart Model Based Coordinated Control Based on Feeder Losses, Energy Consumption, and Voltage Violations

Abstract There can be significant benefits to utilities for implementing automated and controllable devices. However, due to both the cost of smart devices and the cost of implementing the required monitoring, communication, and control, it is often not cost effective to update all devices on the system at once. This article presents an economic evaluation of a model-based distribution control scheme that is independent of circuit topology and integrates legacy and modern control equipment. Distributed engineering workstation simulation results show cost saving to both the customers and utility due to reduction of demand and losses. These cost savings provide the basis for assessing which feeders should be upgraded with smart devices.

Distribution network topology detection with time-series measurements

This paper proposes a novel approach to detecting the topology of distribution networks based on the analysis of time series measurements. The analysis approach draws on data from high-precision phasor measurement units (PMUs or synchrophasors) for distribution systems. A key fact is that time-series data taken from a dynamic system show specific patterns regarding state transitions such as opening or closing switches, as a kind of signature from each topology change. The algorithm proposed here is based on the comparison of the actual signature of a recent state transition against a library of signatures derived from topology simulations. The IEEE 33-bus model is used for initial algorithm validation.

Addressing the challenges for integrating micro-synchrophasor data with operational system applications

This paper describes challenges for integrating high fidelity data with utility distribution operations. Two research projects are described microsynchrophasors (μPMU) and OpenPMU and explore the applications of data for electric power distribution systems.

Abnormal event detection with high resolution micro-PMU data

Power system has been incorporating increasing amount of unconventional generations and loads such as renewable resources, electric vehicles, and controllable loads. The induced short term and stochastic power flow requires high resolution monitoring technology and agile decision support techniques for system diagnosis and control. In this paper, we discuss the application of micro-phasor measurement unit (μPMU) for power distribution network monitoring, and study learning based data-driven methods for abnormal event detection. We first resolve the challenging problem of information representation for the multiple streams of high resolution μPMU data, by proposing a pooling-picking scheme. With that, a kernel Principle Component Analysis (kPCA) is adopted to build statistical models for nominal state and detect possible anomalies. To distinguish event types, we propose a novel discriminative method that only requires partial expert knowledge for training. Finally, our methods are tested on an actual distribution network with μPMUs, and the results justifies the effectiveness of the data driven event detection framework, as well as its potentials to serve as one of the core algorithms to ensure power system security and reliability.