Line Loss Analysis and Abnormal Electricity Consumption Analysis Technology Based on Multi-Source Measurement Fusion

Advance meticulous line loss management through multi-source measurement fusion, precise loss decomposition, and intelligent anomaly identification.

By integrating multi-source data from low-voltage transformer areas—including topologies, meter measurements, load monitoring units (LMUs), and phasor measurement units (PMUs)—this achievement establishes a methodological framework for meticulous theoretical line loss calculation and intelligent analysis of abnormal electricity usage such as electricity theft and meter inaccuracies, making line losses fully quantifiable, decomposable, traceable, and mitigable.

R&D Approach (Independent R&D / Collaborative Innovation)

Independent Innovation

Pain Points and Challenges

1. Complex Line Loss Origins and Insufficient Precision of Traditional Methods
   Low-voltage transformer areas feature complex line topologies and high load fluctuations. Traditional empirical or statistical methods struggle to accurately decouple theoretical line losses, management losses, and abnormal losses, leading to line loss analysis that is “inaccurate and non-transparent.“
2. Highly Concealed Electricity Theft and Metering Anomalies
   Theft methods (such as undervoltage, undercurrent, and phase shifting) and meter inaccuracies are highly concealed, making evidence collection difficult and misjudgment rates high. Relying on manual experience is inefficient and error-prone.
3. Fragmented Multi-Source Measurement Data Lacking Collaborative Analysis
   Data from electricity meters, load monitoring units (LMUs), and phasor measurement units (PMUs) come from diverse sources and operate on different time scales. The lack of a unified data fusion modeling and collaborative analysis mechanism leaves massive data value untapped.

Technical Principles

1. Refined modeling of theoretical line losses
   Based on the topology, conductor parameters, and user load data of low-voltage transformer areas, a theoretical line loss calculation model centered on the voltage drop rate is constructed, achieving precise line loss assessments at both the transformer-area level and the branch level.
2. Mechanism Modeling and Quantitative Calculation of Electricity Theft
   Utilizing data from load monitoring units (LMUs), phasor measurement units (PMUs), and total-to-sub-meter correlations, mathematical mechanisms are modeled for typical theft modes such as undervoltage, undercurrent, and phase shifting. The stolen electricity volume is then quantified through power, current, voltage, and phase deviation calculations.
3. PMU-Enabled High-Precision Line Loss Analysis
   By introducing synchronous phasor measurement units (PMUs), real-time theoretical line loss analysis algorithms for distribution networks are established leveraging high-precision voltage and current phasor data. This significantly enhances the temporal resolution and overall accuracy of line loss calculations.
4. Regression Diagnosis Model for Meter Inaccuracies
   Combining transformer-area load profiles, the power factor of total meters, and common line impedance characteristics, a regression analysis approach is deployed to identify metering deviations, enabling online diagnosis and quantitative evaluation of meter inaccuracies.

Technical Innovation & Highlights

1. Deep Fusion of Multi-Source Measurement Data
For the first time, data from electricity meters, load monitoring units (LMUs), and phasor measurement units (PMUs) are unified into a cohesive model across both distribution networks and low-voltage transformer areas, breaking through the precision bottlenecks inherent in single-source data analysis.
2. Collaborative Analysis of Theoretical Line Loss and Abnormal Electricity Usage
By organically coupling theoretical line loss calculations with electricity theft and meter inaccuracy analysis, this approach achieves a fine-grained, causal decomposition of line loss origins.
3. Quantitative Analysis Method Tailored to Electricity Theft Mechanisms
Rooted in electrical physics and engineering mechanisms, a quantifiable and verifiable analytical framework is established to evaluate typical theft modes, including undervoltage, undercurrent, and phase shifting.
4. Novel Paradigm for Online Diagnosis of Meter Inaccuracies
Leveraging regression analysis of power factors and common line impedance, this approach introduces a groundbreaking technical pipeline that evaluates metering accuracy without requiring physical meter dismantling.

Scenario-Based Applications

1. Lean Management of Line Loss in Low-Voltage Station Areas
   Provides quantitative analysis for transformer areas with abnormal line losses, supporting the formulation of customized and differentiated loss-reduction strategies.
2. Precise Identification and Evidence Support for Electricity Theft
   Assists marketing and inspection departments in rapidly pinpointing suspected power-theft users, reducing manual inspection costs, and significantly increasing the audit hit rate.
3. Meter Operation & Maintenance (O&M) and Metering Management
   Enables online monitoring and early warning of meter inaccuracies, enhancing the proactivity and intelligence level of metering equipment O&M.
4. Digital and Intelligent Support for Distribution Networks
   Provides key algorithms and data foundations for digital twins, fine-grained operations, and intelligent decision-making in low-voltage distribution networks.