Smart Grid Analytics Software Development

High-volume meter data ingestion from your AMI head-end system via REST API, SFTP batch delivery, or direct database connection depending on your vendor's export capability. AMI endpoints using DLMS/COSEM (Device Language Message Specification / Companion Specification for Energy Metering) deliver interval and event data in structured object models -- the pipeline parses these into a normalised schema on ingest. Data validation flags missing reads, duplicate sequence numbers, and implausible values before they enter the time-series store. Gap-filling applies configurable interpolation rules for planned outages and communication failures so downstream aggregations remain consistent.

Time-series storage is selected to match query patterns and scale requirements: TimescaleDB for structured relational queries with time partitioning, Apache Parquet on object storage for long-retention analytical workloads, or InfluxDB where write throughput is the primary constraint. The IEC 61968 CIM (Common Information Model) standard governs the data model for meter and network topology objects, enabling integration with other CIM-compliant utility systems. IEC 61970 extensions cover the energy management system (EMS) data model for generation and transmission assets. Schema designed to handle interval data from millions of endpoints without query performance degrading as volume grows over the deployment lifetime.

Last-gasp event processing detects meter power-loss events and maps outage extent in near real time -- typically within 60 to 90 seconds of the fault, well before customer calls arrive at the contact centre. AMI endpoints transmit a last-gasp message over the mesh network on loss of supply; the pipeline ingests these events, cross-references the network topology from the IEC 61968/61970 CIM model, and constructs an outage boundary from which meters have reported power loss and which have not, isolating the faulted segment to the transformer or feeder section level.

SCADA/EMS integration via IEC 60870-5-101 (serial) or IEC 60870-5-104 (TCP/IP) brings substation breaker and recloser status into the outage picture, and DNP3 connections to distribution automation equipment allow the platform to correlate AMI event data with protective relay operations. Outage boundary estimation identifies the probable fault location on the network single-line diagram. Estimated restoration time is calculated from crew availability, travel time, and historical repair duration for the fault type. Integration with the OMS or field crew dispatch system pushes confirmed outages and crew assignments into the existing operational workflow. NOC dashboards show confirmed outages, estimated customers affected, and restoration status without waiting for calls to define scope.

Voltage deviation detection at meter level uses interval voltage readings reported by AMI endpoints -- typically at 15-minute or 30-minute intervals, with some meters reporting at 1-minute resolution for power quality applications. Sustained over-voltage and under-voltage events are flagged by feeder and transformer, with configurable thresholds aligned to ANSI C84.1 service voltage ranges (Range A: 114V to 126V at 120V nominal) or your regulatory standard.

PMU (Phasor Measurement Unit) synchrophasor data, sampled at 30 to 60 frames per second with GPS time-stamp accuracy of 1 microsecond, gives transmission-level operators continuous voltage angle and frequency measurements for dynamic stability monitoring. Voltage stability indices -- P-V curve proximity indicators and voltage sensitivity factors -- are calculated from real-time PMU streams to identify operating points approaching voltage collapse margins. Power factor analysis and harmonic distortion flagging are applied where AMI endpoints or power quality monitors report power quality data. Voltage profile visualisation across a feeder displays the voltage gradient from substation to end of line, identifying regulators, capacitor banks, or DER injection points contributing to voltage profile deviations. This is the layer that catches voltage regulation issues before they cause equipment damage or customer complaints to the regulator.

Interval data aggregation by feeder, substation, and zone structures the data for planning studies that previously required the data team days of manual preparation. Coincident peak analysis uses actual AMI interval data rather than assumed diversity factors, producing demand estimates that reflect real customer behaviour rather than textbook load shapes.

Short-term load forecasting models -- LSTM (Long Short-Term Memory) neural networks for pattern-based day-ahead forecasts, and Prophet for trend-seasonal decomposition applied to feeder-level demand -- produce 24-hour and 7-day load forecasts with confidence intervals that planning engineers can act on. Demand response programmes using the OpenADR 2.0b protocol for automated device control and event dispatch are supported with pre- and post-event measurement and verification reporting that quantifies actual demand reduction against baseline. DER impact modelling for solar and battery installations uses measured net consumption profiles from AMI data to assess the capacity displacement on affected feeders and transformers. Transmission congestion management analytics identify binding constraints by correlating hourly load flows with LMP (Locational Marginal Price) signals from your energy market, supporting congestion cost attribution and capacity planning prioritisation. Load growth trend analysis at the feeder level identifies capacity constraints before they emerge so capital works planning is driven by data, not peak-year extrapolation.

Feeder loading dashboards show real-time and historical load against thermal rating for each feeder and transformer in the network, with utilisation flagged at configurable thresholds (typically 80%, 90%, and 100% of nameplate capacity) to give asset managers early warning of approaching constraints.

Reliability metrics calculated automatically from outage event data include SAIDI (System Average Interruption Duration Index), SAIFI (System Average Interruption Frequency Index), and CAIDI (Customer Average Interruption Duration Index) -- calculated at feeder, zone, substation, and network area level for any selected time period and formatted for both internal review and regulatory reporting obligations. Where AMI last-gasp data is the primary outage detection source, the platform also calculates time-to-awareness (the interval from fault occurrence to NOC notification), a metric that demonstrates the operational value of AMI investment. NERC CIP (Critical Infrastructure Protection) cybersecurity compliance requirements affect how grid data is handled, transmitted, and accessed; the platform's access control, audit logging, and data classification layers are designed to meet CIP-007 and CIP-011 requirements for low and medium-impact systems. Trend views for reliability performance over rolling 12-month periods support regulatory submissions and capital works justification. Operational dashboards for the NOC and planning dashboards for network engineers are built from the same underlying data, filtered and visualised for each audience's decision-making context.

Consumption pattern analysis identifies anomalies consistent with meter tampering, illegal connections, or metering errors by comparing individual meter profiles against expected consumption patterns derived from similar customers, weather-adjusted baselines, and historical profiles for the same account. Sudden consumption drops, zero-read sequences outside planned outages, and consumption patterns inconsistent with the tariff class or load category are all flagged for investigation review.

Energy balance calculations compare distributed generation and import at the transformer level against the aggregate of downstream meter reads, calculating the technical-loss-adjusted balance for each transformer service area. Residual losses exceeding your technical loss expectation by a configurable threshold are surfaced as probable NTL incidents with a confidence score, the contributing transformer zone, and the candidate meter list ranked by anomaly severity. Loss attribution by network segment focuses investigation resource on the highest-loss areas first. Meter health flags from read quality indicators -- missing reads percentage, communication failure rates, and CRC error rates from DLMS/COSEM communication logs -- identify meters due for physical inspection independent of consumption anomalies. The islanding detection module identifies distributed generation operating in island mode during grid outages, which creates both safety risks and metering errors in net-metering configurations. This is the analysis layer that turns AMI data into an NTL detection tool without requiring manual inspection of thousands of individual meter records.