By Satish Saini, HEXstream utilities industry specialist  

Performing the proper data analytics plays a huge role in accurately detecting losses across various nodes of the grid. Proper data analytics gives utilities and decision makers deeper insights and greater tools with which to develop the best collaborative strategies to take energy-loss-mitigation measures.

Let’s explore the key elements… 

1. Data collection: Collect energy / power-flow data at all relevant and available nodes of the grid from multiple sources (including incoming, substations, feeders, feeder sections, field distribution transformers, LV distribution system and end customer). This data should be collected straight from the applicable technology platform (revenue-grade metering, SCADA, EMS, DMS / ADMS, AMI/MDMS, sensors and others), without much human intervention or manipulation.

2. Data validation: Review and ensure data accuracy and remove any errors or inconsistencies. In addition, check and confirm the calibration of the devices to ensure accurate recording of energy-flow data and remove any detected discrepancies.

3. Big data analytics: Perform loss analysis using relevant tools for energy received, supplied / distributed and billed to the consumers. This may include implementing advanced artificial intelligence (AI) and machine learning (ML) algorithms to identify patterns indicative of theft of energy / non-technical losses.

These analytics may include energy-balance method for various defined accounting units across the grid:

• Substation-level accounting unit analysis

• Feeder-level accounting unit analysis

• Distribution-transformer (DT) level accounting-unit analysis

• Geographical area / community-level analysis

• Customer-level analysis

4. Identifying gaps in energy flow: Using the above methodology, identify suspected areas on geographical basis or grid-assets infrastructure basis; this may include detailed analytics for any levels of the grid infrastructure per the availability of the data.

5. Implementing pilot projects, field audits and inspections: Deploy further automatic measuring devices / metering along with any automation technologies at more upstream and downstream assets of the grid and repeat the exercise using above steps to go to granular levels performing data analytics.

Conduct physical-site inspections and audits in areas and infrastructure feeding suspected segments and customers where data analytics indicates potential non-technical losses. A close collaboration between the field and data-analytics teams is the key to accurately detect, quantify and reduce losses.

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