Encyclopedia of Wireless Networks

Living Edition
| Editors: Xuemin (Sherman) Shen, Xiaodong Lin, Kuan Zhang

Advances in Distribution System Monitoring

  • Omid ArdakanianEmail author
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-32903-1_243-1



Distribution system monitoring refers to key technologies for monitoring wide-area power distribution systems, between the substation and customer meters, to facilitate distribution grid planning and operation.


Power distribution systems had a simple design historically. With radial topology, one-way power flow, and predictable demand curves, distribution system planners and operators were only required to evaluate the envelope of design conditions, such as peak loads and fault currents, to ensure reliability and power quality. Thus, there has been little need for costly telemetry beyond the substation, which was remotely monitored through supervisory control and data acquisition (SCADA) at several-second intervals. SCADA is a technology that has been in place for decades to connect sensing and control nodes, mostly in the transmission system, to a control room (Northcote-Green and Wilson, 2006) using dedicated telephone lines, cellular, radio, or power line communications.

In recent years, the rapid growth in the deployment of controlled loads and distributed energy resources (DER) has led to unprecedented amounts of variability and uncertainty, which complicate distribution system planning and operation. This has necessitated more comprehensive monitoring of distribution circuits, and a novel planning and operation paradigm centered around pervasive monitoring, real-time analytics, and closed-loop control (Ardakanian et al., 2016).


In light of new and complex grid dynamics that span multiple timescales, the operators must closely monitor the voltage and current waveforms at different locations to detect and characterize certain behaviors, such as harmonics and oscillations, which could not be observed by the traditional SCADA system. This calls for an advanced distribution system monitoring solution which
  • has low cost and complexity of deployment,

  • covers both primary and secondary distribution networks,

  • provides high-resolution measurements at multiple locations,

  • uses precise time synchronization so that measurements can be compared across locations,

  • utilizes a communication network that provides
    • stable and high bandwidth to allow for transferring high-sample-rate measurements from thousands of end nodes to upstream aggregation nodes and decentralized controllers,

    • low latency which is necessary for real-time applications, such as situational awareness, fault detection, and automated demand response,

    • high degree of reliability, or, equivalently, low link outage and packet loss probabilities,

  • incorporates mechanisms to prevent unauthorized access, data manipulation, and denial of access to the sensing and control nodes,

  • leverages an extensible data processing pipeline, high-throughput data stores, and real-time event triggers to provide a deeper insight into the operating state of the grid.

Two technologies have emerged in the last couple of years, namely, smart meters and distribution-level phasor measurement units (D-PMUs), which together can constitute the data acquisition layer of a well-suited distribution system monitoring system that meets the above requirements.

These technologies differ mainly in physical quantities they can measure, the time granularity of data, the location of sensors, and communication requirements. Smart meters are deployed at customer premises, recording their energy usage and voltage level hourly or more frequently (up to every 15 min). The meters send the recorded data at regular intervals to the utility’s data center using wireless or wired communications (Gungor et al., 2011). The smart meters, communications networks, and the utility’s data management systems are collectively known as advanced metering infrastructure (AMI).

The D-PMUs (NASPI Distribution Task Team, 2018) offer higher precision and resolution than smart meters. They sample voltage and current waveforms at a high frequency (usually at 120 Hz) and assign a precise time stamp to the measured quantities. The measured quantities are typically voltage and current phasors along with frequency, where a phasor represents the magnitude and phase angle of voltage or current. The D-PMUs are installed on distribution circuits supplementing the existing SCADA system by monitoring the network downstream of the substation. The data streams produced by a network of D-PMUs, termed phasor network, are aggregated by a small number of phasor data concentrators (PDCs), enabling fast comparison of time-synchronized phasor measurements from multiple locations before they reach the utility’s data center. Given the bandwidth requirement of a phasor network, a broadband cellular network technology, such as 4G, is typically used for sending phasor measurements to the utility.

The smart meters and D-PMUs complement each other in the sense that they respectively monitor the end nodes and intermediate nodes in the distribution network. Moreover, in some applications, the availability of smart meter data can compensate for the lack of higher-resolution phasor measurements at some upstream nodes.

Implications for the Grid Planning and Operation

Distribution system operators can utilize the fine-grained measurements along with appropriate analytical tools for many important applications that concern planning and operation of the distribution system (Meier et al., 2017). These applications are outlined below:
  • Topology detection is to determine the set of energized lines and the status of switches to understand the real-time operational structure of the distribution network.

  • Phase identification is to identify and track the connectivity and loading of the three AC phases throughout the network.

  • Model parameter estimation is to compute impedances of distribution components, such as line segments and transformers, and update the distribution system model.

  • State estimation (Abur and Expósito, 2004) is to determine unknown state variables, for example, voltage magnitudes and phase angles at specific buses, given a set of known or measured state variables.

  • Distributed Generation (DG) characterization is to separate the net-metered distributed generation from load.

  • Event detection and localization is to detect and classify short-term operational and power quality events and pinpoint them to a small part of the distribution network.

  • Equipment health monitoring is to detect and monitor equipment health issues or early signs of equipment aging to prevent damage to the equipment and support system upgrade decisions.

  • Outage management is to automatically and reliably detect outages to reduce their duration and the system restoration cost.

  • Phasor-based control is to incorporate phasor measurements in the feedback control of distribution system components and active end nodes, e.g., solar inverters, battery storage systems, and electric vehicle chargers.

Table 1 describes which physical quantities must be measured and what time granularity is sufficient for each of these applications.
Table 1

Applications of distribution-level phasor measurement units


Data streams


Minimum resolution

Topology detection

Voltage phasors


1 cycle

Phase identification

Voltage phase angle


1 s

Model parameter estimation

Voltage and current phasors


1 s

State estimation

Voltage and current phasors


1 s

DG characterization

Voltage and current phasors


1 min

Event detection/localization

Voltage and current phasors


1 cycle

Equipment health monitoring

Voltage and current phasors


1 min

Outage management

Voltage and current magnitudes


1–15 min

Phasor-based control

Voltage phasors


1 cycle



  1. Abur A, Expósito A (2004) Power system state estimation: theory and implementation. Power engineering (Willis). CRC Press, Boca RatonGoogle Scholar
  2. Ardakanian O, Keshav S, Rosenberg C (2016) Integration of renewable generation and elastic loads into distribution grids. SpringerBriefs in electrical and computer engineering. Springer International Publishing, ChamGoogle Scholar
  3. Gungor VC, Sahin D, Kocak T, Ergut S, Buccella C, Cecati C, Hancke GP (2011) Smart grid technologies: communication technologies and standards. IEEE Trans Ind Inf 7(4):529–539CrossRefGoogle Scholar
  4. Meier A, Stewart E, McEachern A, Andersen M, Mehrmanesh L (2017) Precision micro-synchrophasors for distribution systems: a summary of applications. IEEE Trans Smart Grid 8(6):2926–2936CrossRefGoogle Scholar
  5. NASPI Distribution Task Team (2018) DisTT: synchrophasor monitoring for distribution systems: technical foundations and applications. Technical report, North American Synchrophasor Initiative. https://www.naspi.org/node/688Google Scholar
  6. Northcote-Green J, Wilson RG (2006) Control and automation of electrical power distribution Systems. CRC Press, Boca RatonGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computing ScienceUniversity of AlbertaEdmontonCanada

Section editors and affiliations

  • Vincent Wong
    • 1
  1. 1.Department of Electrical and Computer EngineeringThe University of British ColumbiaVancouverCanada