Managing Data: MDMS and MDAS improve utility operations

MDMS and MDAS improve utility operations

With growing technological advancements, consumer meters have become a storehouse of a large quantum of useful information, which allows utilities to enhance their operational efficiency. Meter data acquisition systems (MDAS) and meter data management systems (MDMS) help in consolidating the data from meters. These systems are used to derive meaningful results using data analytics algorithms, which can help utilities in undertaking demand response, detecting meter tampering, managing outages, ensuring theft protection, etc. This would go a long way in providing 24×7 reliable and quality power to all.


The main objective of an MDAS is to acquire data from meters within the distribution system and consumer meters for system performance monitoring and decision-making, network analysis, system planning, and monitoring of consumer energy usage for billing and customer relationship management and detection of tampering and outages. Broadly, an MDAS comprises a communication server application, which establishes communication with the modem associated with the data concentrator unit and processes the data sent by the device. Further, the communication server reads the raw data received by the communication server application and converts it into useful meter data. Through a web-based user application, users can log in and view their meter reading. Besides, a utility dashboard can be used as an interface for supervisory activities. Meanwhile, an MDMS consolidates the metering- and consumption-related data from all the sources in a centralised system. It standardises the data according to utility-specific rules, making it suitable for a wide range of utility operations. It is also useful for sending alerts for meter-based conditions of interest such as usage patterns, events and system performance. Further, it interconnects the metering system with different enterprise applications.

Improving operational efficiency

Data analytics is one of the primary objectives of implementing an MDMS. The embedded analytics in an MDMS include processing meter readings, ensuring that the data is current and valid, and issuing alerts in case of configured conditions of interest. Meanwhile, extended analytics can be undertaken to leverage valid data for complex analysis by combining core business logic of master data management (MDM)  with additional correlations critical to the utility.

An MDMS helps in undertaking business analytics, and deciphering meaningful trends from the meter data. It provides valid, complete and uniform data for improving customer service, operating consumer portals, and undertaking distribution planning and tariff analysis. Further, an MDMS manages the commands from the downstream. It also helps in real-time event management and notifies voltage anomalies, outage/restoration and tampering. Besides, the data gathered from the system helps in conducting historical/predictive analysis. This helps in maintaining secure and comprehensive information to achieve business objectives. An MDMS provides accurate information, which helps in enhancing consumer satisfaction and meeting consumer expectations. It also helps in improving the operations of a discom through improved asset management and quick response during a power disruption.

An MDMS plays a significant role in improving the efficiency of the outage management system (OMS).  It helps in eliminating unnecessary field visits by filtering false outages. In fact, outages are often resolved even before the customer is affected. This enhances the overall efficiency of the field crew despatch process upon the occurrence of an outage. An MDMS also restricts  unnecessary outage notifications sent to the OMS using intelligent filtering. The system filters outage events that have been confirmed at the feeder level, validates restorations and pinpoints nested outages. Improved consumer billing is another important feature of an MDMS. It processes and stores data and therefore serves as a data repository. It forms a vital component of validation, estimation and editing of meter reading. It validates the meter reading, estimates invalid or missing readings, issues alerts in case of any apparent exceptions in the meter reading, etc.

In sum, electricity consumers are at the core of discom revenues and hence, their consumption pattern analysis is vital. Further, as consumers are now becoming prosumers, the use of MDMS and MDAS is important.

Based on inputs from a presentation by Subhadip Raychaudhuri, Deputy General Manager, Tata Power Delhi Distribution Limited