With growing technological advancements, consumer meters have become a storehouse of relevant information, allowing utilities to enhance operational efficiency. Meter data management systems (MDMSs) 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, etc. The MDMS consolidates metering, consumption and other related data from all the sources in a centralised system. It standardises data according to utility-specific rules, making it suitable for a wide range of 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.
The MDMS architecture has three layers. The first is the communication stack, which could include radio frequency, transmission control protocol/internet protocol and a hand-held unit (HHU). The data in the HHU can be collected for three-phase consumers or high-end consumers and used in analysis. The second layer includes an application that takes data as input, known as the head-end system. The third layer stores the data, analyses it and creates reports. The analysis includes a wide spectrum of topics such as meter tampering, loss reduction, demand report, and ways to curtail demand or demand aggregation. At each level, the System Average Interruption Duration Index/System Average Interruption Frequency Index is calculated based on the meter data, starting from a single-phase consumer to an extra high voltage consumer. However, an important factor is the size of data that can be used in the MDMS. If the system is overloaded with data, the chances of its becoming slow are higher. The performance of the algorithm is based on the input data, so there are more chances of the algorithm being affected due to a large data size. Therefore, the data needs to be filtered in terms of its importance depending on how it would contribute to the aggregate value.
Core features and functions
The key features in the MDMS include collection of data, validation of data, security, data processing and data management. Data collection is done through a head-end system and not through meters. Data security is crucial. The system needs to be compatible with the existing one in order to process data for operations and maintenance, and manage it for billing, procurement planning, etc. Data management and processing are value-added services coupled with the service bus and the external system. Data analytics for big data can be categorised into three broad segments: descriptive and inferential statistics, machine learning and visualisation. Descriptive and inferential statistics is most commonly used and includes a set point, hypothesis testing, Z&T scores and chi-square testing. Machine learning involves linear and logistic regression, classification and association, autoregressive integrated moving averages and neural networks. Visualisation includes histograms, box plots, interactive charting, aggregation and a dashboard. The core features of the system include export adaptors, flexible APIs (application programming interface) for meter data access, import/export management, validation, estimating and editing engines, security management, route and cycle management, metering system integration, and configuration management.
Data can be captured from multiple sources and transmitted to the utility’s other IT systems such as custom integration solutions (CIS) and outage management system (OMS). A consolidated interface for data management is present along with automated data synchronisation across utility systems. The system also has a pre-built integration with head-end and advanced metering infrastructure (AMI) systems. The currently available tools for integration with the MDMS include software such as SAS, pentaho, SPSS, R, Minitab and SAP. Some of the companies providing MDMS are Oracle, Siemens, Gartner, Itron, SAP and Infosys.
In sum, consumers are at the core of discom revenues and hence an analysis of their consumption pattern is vital. With consumers becoming prosumers, the use of MDMS has become even more key.
Based on a presentation by BSES Yamuna Power Limited at a Power Line conference