In today’s competitive environment, there is an increased pressure on the distribution network operators to manage the state of the networks in real time to ensure reliable services. The regulators have enforced stringent guidelines and performance benchmarks (such as CAIDI, CAIFI, CI and CML), and there are heavy penalties for not adhering to these standards. This combined with the growing awareness of the customers of their rights to demand better services under the new electricity regulations have driven the distribution utilities to introduce innovative ways of managing their networks more efficiently and effectively. As a result, sensor-based technologies have assumed significance in managing low voltage networks down to the last mile.
Some of the key application where sensor based technologies are being used by the utilities for improving operations, revenues and energy efficiency are:
Asset management system
Transformer monitoring system
Fault management and service restoration
Real time network analysis
Power quality monitoring
Peak load management
Automated demand response.
Asset management systemOne of the main challenges of the distribution utilities is track their network assets throughout the life cycle, in order to manage assets costs with greater efficiency and higher profitability. Utilities are exploring new technologies for precise inventory control, with the ability to manage, track and secure critical assets in real-time, as part of the strategy.
The most commonly used technology is the “wireless” tracking devices. Tiny wireless RFID (radio frequency identification) tags can be placed on a network asset such as distribution transformer or smart meter. These RFID devices communicate with the intelligent asset management system, which helps the utilities in asset planning, deployment, tracking and optimisation. The active RFID tags are attached to assets which are to be tracked or monitored. These tags communicate with RF sensors strategically located near the assets and linked via wireless repeaters or a data communication bus to the asset management application, which then displays the real-time location of the tagged assets. The complete history of an asset or its movement is logged by the system through the use of active asset tags.
Transformer monitoring systemDistribution transformer is the heart of the LV distribution networks. The health of the Distribution transformer has to be monitored at all times to ensure continuous and reliable supply of electricity services. Introduction of sensors for on-line monitoring of key operating parameters reduces the risk of transformer failure and cuts maintenance costs.
The parameters which can be monitored on a Distribution transformer are:
Transformer oil level
Gas and moisture in transformer oil.
Monitoring of the above parameters involves on-line collection of data using sensor based measurements and transmitting the data to the remote monitoring application through suitable communication systems e.g. RF or ZigBee communication. The failures of transformers in service are broadly due to temperature rise, low oil levels, over load, poor quality of connections or improper installation.
Monitoring sensor data of distribution transformer for critical parameters of surface temperature, low oil level and over load could be utilised to take proactive action in fault prevention, thus increasing the reliability of distribution network.
Fault management and service restorationFault passage current sensors on LV distribution systems can measure the current flow in real time and help in the early detection of overloading, short circuit or earth fault. The current signals can be graphically displayed on a remote Digital Fault Recorder (DFR) and the information could be utilised to validate the location of possible fault occurrence. Early detection of an impending fault can provide operators with a better understanding of the vulnerable sections of the network and the maintenance crew can be dispatched to reinforce those sections before a catastrophic fault may occur.
There is increased pressure from regulators and customers to reduce the number and duration of outages. Imposition of stiff penalties on utilities for poor network performance is incentivising the use of sensors for better management of power distribution system, early fault detection and pre-empting power outages. Utilities are therefore considering deployment of current sensors (Rogowski Coils, Hall Effect sensors) for better fault management and achieving regulatory targets of network performance through:
Quicker detection of a fault condition
Accurately determining the location of fault
Isolating of the faulty section of the LV network
Re-energising healthy sections – upstream and downstream – outside the isolated faulty section.
Any abnormal data from the sensors are analysed and used to isolate the faulty sections and switch to alternate network plans to minimise the impact of power disruption and facilitate early restoration of services in case of a fault. The sensor data help in optimal design of the switching plans of LV networks, considering all network constraints, system interlocks, protective devices and safety issues, and facilitate early restoration of services to a large part of the network and customers, without overloading.
Real time network analysisEarlier, for traditional distribution networks without sensor-backed automation, utilities had to rely on customer calls to be aware of network outages. Now Supervisory control and data acquisition (SCADA) at the substation get regular data from remote sensors via remote terminal units (RTUs) in real time, which is analysed to know the state of the networks.
Sensor-based technologies have made predictive analysis possible on the electrical networks which helps in network fault prevention, optimisation and planning. With real time analysis, it is possible to detect sudden sags or swells in feeder voltages and current, any abnormal load variations or physical conditions. Integrated with Transformer monitoring system, outage management system and electrical protection systems, real time analytics can help estimate the current state of the network and identify the characteristics which might need immediate attention to prevent major failures.
The intelligent Distribution Management System (DMS) rely on sensor data for real-time modelling of the distribution network. Signals from the fault sensors, help the DMS perform real time analytics to operate the protective devices in a coordinated manner to isolate the faulty sections and restore the network through alternate switching plans, in a safe and reliable manner.
Power quality monitoringThe quality of electrical power is an issue of increasing concern for industry players. The power quality of an electrical distribution network is affected by power line disturbance such as wave shape faults, overloading, capacitor switching transients, impulse transients or harmonic distortions. The rapid proliferation of energy efficient equipment, renewable energy sources and power electronics is increasing the presence of harmonics in the electrical supply. This can often damage circuits and equipment, by overheating and failure, or by the inefficient use of increasingly expensive energy. Ideally, the best electrical supply would be a sinusoidal waveform of a constant magnitude and frequency. However, many loads are not purely resistive and the presence of magnetising current, effect of rectification and inherent impedance of certain loads may result in creation of harmonics or transients, which may degrade the power quality and cause technical losses.
Various measurement instruments of smart grids e.g. smart meters, protection relays and fault recorders may not measure all the power quality parameters. By using appropriate sensors and telemetry systems, it is possible to monitor power quality problems at regular intervals and analyse these data to reduce their effects, thus making the electrical network trouble free and more efficient. These sensors allow distribution network operators and high electrical load consumers to record vital information regarding power quality.
Sensor-based technology solves energy quality problems by timely identification of specific sources of harmonics. Each sensor unit measures and records harmonic and inter-harmonic frequencies, present on the main electricity supply at specific locations. The recorded data is then periodically transmitted through a wireless or wired communications network to a centralised database, where the information can be analysed and stored. The low cost of each sensor, combined with the convenience of wireless communication, enables monitoring electrical power quality at multiple locations of the network. This method significantly reduces costs by eliminating expensive diagnostic instrumentation, such as power quality analysers.
The sensor platform incorporates data management and visualisation software, which allows maintenance and operation personnel to use it for power quality measurements and analysis.
Peak load managementSensors are transforming the operation of LV networks in combination with information and communication technologies (ICT) to build intelligence into the network for peak load management. Modern applications in energy generation, power distribution and energy consumption use sensors to make efficient use of green energy, increase automation in distribution and enable peak load management.
Interconnecting consumer devices with the home area networks, and at the same time, communication with the utility networks through a home gateway facilitate residential energy management. Residential energy management uses utility-driven price signals which vary depending on the time of the day, called Time of Use (ToU) pricing. In TOU pricing, electricity consumption during peak hours costs more than electricity consumption during off-peak hours. In peak hours, demands of the consumers rise, and utilities are compelled to deploy spinning reserves at a higher cost of energy and environment. Reducing peak load decreases the expenses for energy generation with corresponding decrease in greenhouse emissions.
Wireless sensors can play a key role in sensing the growth in energy demand and prompting actions to control this demand during peak hours. Intelligent electronic appliances fitted with sensors can communicate with the electric grid in real time to switch off or defer operation to cheaper off-peak hours, thus helping in energy balancing during peak loads. Another faster and reliable way of managing peak loads and balancing demands is through automated demand response.
Automated demand responseAutomated demand response refers to a smart grid device or application interacting with customers to influence their consumption of electricity or their load demand during select time periods. This signals customers to decide to lower their consumption or shed electricity during peak periods, and shift their demand to off-peak periods to save energy costs. Utilities use automated demand response to achieve a balance between electricity generation and electricity consumption, thus helping in load optimisation and grid stability.
Traditionally demand response interactions were manual, but with the introduction of sensors and advanced control systems, the LV network interacts directly with its customers’ load control systems to manage peak loads and balance consumption. Automated demand response combines the inherent benefits of automation to bring more reliable, faster and cheaper responses to the load demand signals.
Automated demand response requires both the grid and the demand-side entities to install infrastructure to support the exchange of signals. The grid entity puts in place sensors capable of communicating demand response signals to their customer’s automation equipment and the customer installs equipment capable of receiving these signals. Further, the demand response signals are relayed to the control systems where demand response strategies have been pre-programmed to execute the appropriate load control. Depending on the type of customer facility, such control systems could be as simple as a thermostat in a residence or as sophisticated as an industrial process control system. The smart network will receive feedback of the demand response signal on the facility’s consumption via a smart meter or the control system.
With automated demand response, the customer can respond to smart meter or sensor signals indicative of desired levels of demand response as opposed to manual load control. Automated demand response represents a way for distribution network operators to avail of more demand-side resources as a cheaper option for grid balancing.
ConclusionThe assessment studies on the impact of sensor technology on LV distribution networks reveal that the technology has a high potential in improving operational efficiencies through proactive fault management, improving power quality, network reliability and controlling technical losses. Other advantage of sensor-based technologies is the contribution to the reduction of greenhouse gas emissions, being able to maintain the health of LV networks in a sustainable and energy-efficient manner. Sensor based applications being used in smart power grids and combined with demand side management contribute to efficient use of energy resources and optimised network operation, thus helping to reduce the carbon footprint.
Jayant Sinha, Lead Consultant (Smart Networks), Enzen Global Limited, United Kingdom