Sensor Technology in e-balance
Fault detection and location in the Low Voltage (LV) distribution grid is still inefficient due to the lack of LV grid monitoring. Presently, LV fault detection relies on customer calls informing the Distribution System Operator about outages, and the faults are located based on inspections made by maintenance crews. LV grid monitoring will enable deploying fault detection and location features, by sending alarm notifications once a fault is impending or detected through the correlation of real-time data from the wireless sensors deployed along the LV feeder. A Public Light (PL) monitoring suffers from the same fault vulnerability of conventional LV grid segments. PL fault detection also relies on complaints from clients or on visual inspections made by maintenance crews. PL monitoring will also enable deploying fault detection and location features.
In the e-balance project, the monitoring of the LV grid is done by sensors, which communicate via an RF-mesh wireless sensor network. The sensors are deployed in strategic points of the LV grid, like secondary substations and along their respective feeders. The sensor measurements, e.g., current, voltage and power, are communicated to a secondary substation controller, which runs the appropriate algorithms to detect LV grid faults and localize them. The complete system will be demonstrated in the EDP Distribuição LV grid in the region of Batalha in Portugal. The architecture of a sensor node is shown in Figure 1.
As seen in the figure, the main modules of the sensor node are:
- LV Sensing Module, which measures the voltage, current, active and reactive power of the 3 phases (L1, L2, L3) of the LV feeder;
- PL Sensing Module, which measures the voltage, current, active and reactive power of the Public Light feeder;
- Voltage/Current Fault Detector module, which detects the voltage and current faults of the LV feeder and PL feeder;
- Radio Frequency (RF) Mesh Module, which contains the control processor with very low consumption, the radio module and runs the operating system;
- AC/DC power module.
Low power consumption and energy storage mechanisms are needed in these components as they are required to transmit alarms to the secondary substation controller up to 20 seconds after they lose power. The use of batteries is not recommended due to its reduced live and for ecological reasons, supercapacitors are used instead.
The RF Mesh modules obey to the IEEE 802.15.4 standard, which specifies operation in the 868 MHz and 2.4 GHz frequency bands in Europe. The frequency of 868 MHz ISM band is chosen due to its lower attenuation in open space, when compared with the 2.4 GHz ISM band.
An 8 bit micro-controller is chosen due to its very low consumption, while being able to run the Contiki operating system. The decision to use Contiki in the RF Mesh Modules is based on the following reasons:
- Strict power requirements.
- Last gasp communication requires ultra-low power consumption from the micro-controller;
- Already offers 6LowPAN and RPL.
LV and MV Grid resilience features in e-balance
Dynamic distribution grids are prone to faults and to energy losses. Resilience is of utmost importance as reliability and quality of service are goals for deploying future proof smart grids. Distributed automation plays an important role towards those grids, supported by LV grid sensors and smart meters, as well as by MV substation centric and dispatch centre innovative applications. The e-balance architecture brings the needed basis for deploying grid resilience features.
A set of algorithms were specified, designed and implemented, suitable for grid resilience, comprising self-healing and optimal operation for MV grids, as well as grid monitoring features, fault prevention, fault detection and location, overvoltages mitigation and fraud detection for LV grids, among others.
The algorithms performing fault location and detection rely on real time data provided by the LV Sensors. Those algorithms are implemented at the LVGMU – LV Grid Management Unit – master controller, which is a DTC, acronym for Distribution Transformer Controller. The DTC interfaces the LV Sensors via a RF Mesh gateway placed at the secondary substation.
The main features implemented correspond to several Use Cases (UC) targeted for LV grids resilience improvement. They are as follows:
- UC13 – Neighbourhood power flows
- UC14 – DER power flows
- UC18 – Quality of supply measurement
- UC20 – Fraud detection
- UC21 – Losses calculation
- UC22 – LV fault detection and location
- UC23 – Public lighting fault detection and location
- UC24 – Fault prevention
Regarding the MV grid, other algorithms were implemented, which also provide resilience features, corresponding to several Use Cases (UC) targeted for MV grids. Among them, the following are highlighted:
- UC15 – Optimized Power Flow
- UC17 – Validation of Optimized Solutions
- UC29 – MV Fault Detection and Location
- UC30 – Self-healing
The Optimized Power Flow feature aims at improving the MV grid energy efficiency, by presenting to the TLGMU (Top Level Grid Management Unit) user a set of switching orders suitable for mitigating the MV grid losses. The TLGMU corresponds, indeed, to a SCADA/DMS (Supervisory Control and Data Acquisition / Distribution Management System). Those switching orders will allow such user to change the MV grid topology, so that the new operating topology contributes for optimized power flows, thus mitigating the overall power losses in the MV grid, granting the same level of service quality. The way the switching orders are performed relies on the Validation of the Optimized Solutions initially suggested. This is a feature provided by Use Case 17, which is transparently embedded in the feature provided by Use Case 15.
The MV Fault Detection and Location and Self-healing features can be deployed also at TLGMU or at MVGMU (MV Grid Management Unit) level. The MVGMU corresponds, indeed, to primary substation grid area controller, also called Smart Substation Controller – SSC. In the case of e-balance, those features were installed at the MVGMU.
The implementation of both the TLGMU and the MVGMU was carried out over a virtualized platform where a simulated RTU (Remote Terminal Unit) was also included, aiming at providing grid fault simulation capabilities.
Below, a sequence of MV fault detection and location followed by self-healing is shown.
Figure 3: Geographical view and a synchronized MV feeder diagram, showing the initial grid state, fed by São Jorge primary substation (yellow colour)
Figure 4: Indication where the MV fault will be simulated
Figure 5: Geographical view and a synchronized MV feeder diagram, showing the non-energized feeder (in white colour) as a result of the fault
Figure 6: Geographical view and a synchronized MV feeder diagram, showing the new feeder topology upon self-healing, showing the isolated faulty segment (in white), the upstream reenergised feeder (in yellow) and the downstream service restore (in blue)