Maria Ebrahimi
Maria Ebrahimi
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Power Quality Management

Power Quality Management

Power Quality Management

PQM is a monitoring platform that measures solar panel output against international power quality standards in real time.

ROLEFrontend Developer
PLATFORMWeb Application
INDUSTRYRenewable Energy
Timeline4 Months
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Project Overview

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01

Real-Time Power Quality Compliance, Standardized

PQM is a monitoring platform that measures solar panel output against international power quality standards in real time. As frontend developer, I built the interface layer, compliance visualization, and rendering pipeline that let operators instantly see which panels meet standard and which don't.

A complete digital transformation for auto service centers
ROLEFrontend DeveloperEnd-to-end ownership
PLATFORMWeb AppDesktop + Mobile
INDUSTRYRenewable EnergySolar Power Quality
GOALStandards Compliance MonitoringReplace manual audits

Key Screens

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02
Built for accuracy and instant compliance checks
Each view was validated against real sensor data and certified compliance thresholds.
Real-Time Power Quality Dashboard
Harmonic Distortion Waveform Viewer
Panel-by-Panel Compliance Overview
International Standard Comparison View
Voltage & Frequency Deviation Charts
Automated Non-Compliance Alerts
Site-Level Aggregated Reporting
Exportable Compliance Certificates
Historical Trend & Degradation Analysis
Multi-Site Fleet Health Overview

Project Problem

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03

Manual compliance checks were costing time, accuracy ,and regulatory confidence.

Before our solution, verifying panel compliance with international power quality standards relied on manual spreadsheet analysis and periodic offline audits. This resulted in delayed detection of non-compliant panels, inconsistent reporting, and no real-time visibility into fleet-wide power quality.
High latency between a compliance issue occurring and it being detected
Manual spreadsheet-based analysis prone to human error
No standardized way to compare readings across different international standards
Lack of real-time alerts for harmonic distortion or voltage deviations
Non-intuitive raw data exports hindering fast, confident decision making

Stakeholder Needs

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04
What business owners needed
Through discovery interviews with owners and quality engineers, four critical requirements emerged.

Standards Coverage

Support for IEC 61000, IEEE 519, and EN 50160 without separate tools for each.

Real-Time Accuracy

Sub-second detection of harmonic distortion and voltage deviations.

Fleet-Wide Scalability

Architecture capable of monitoring hundreds of panels across multiple sites.

Audit-Ready Reporting

Exportable, certifiable compliance reports generated directly from live data

Customer Needs

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05
What plant operators actually needed
Surveying plant operators revealed clear expectations that no existing tool was currently meeting.
Instant visibility into whether panels meet international power quality standards
Clear, non-technical explanations of what a compliance failure means
Exportable reports for audits and regulatory submissions
Reliable performance even when monitoring hundreds of panels simultaneously

Design Process

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06

RESEARCH

Studying IEC 61000 & IEEE 519 standards to define compliance thresholds

Data Architecture

Structuring harmonic distortion, voltage, and frequency datasets for fast querying

Prototyping

Technical spikes for waveform rendering and compliance scoring logic

Compliance Engine

Building a rules engine to validate readings against multiple international standards

Development

Real-time charts, threshold indicators, panel comparison views

Testing

Validating calculations against certified reference datasets

Handoff

Documentation, deployment, and monitoring setup

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  1. RESEARCH

    Studying IEC 61000 & IEEE 519 standards to define compliance thresholds

  2. Data Architecture

    Structuring harmonic distortion, voltage, and frequency datasets for fast querying

  3. Prototyping

    Technical spikes for waveform rendering and compliance scoring logic

  4. Compliance Engine

    Building a rules engine to validate readings against multiple international standards

  5. Development

    Real-time charts, threshold indicators, panel comparison views

  6. Testing

    Validating calculations against certified reference datasets

  7. Handoff

    Documentation, deployment, and monitoring setup

The Solution

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07
Four engineering pillars that transformed quality monitoring

Real-Time Compliance Checks

[01]

Every reading is validated against international standards the moment it's ingested.

Performance at Scale

[02]

Reduced waveform rendering time by 65% using downsampling and canvas rendering.

Actionable Alerting

[03]

Replaced raw threshold triggers with time-windowed analysis to cut false positives.

Standards-Agnostic Engine

[04]

Built a configurable rules engine that supports multiple international power quality standards.

Learning & Reflections

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08
01

Standards Compliance Is a Data Modeling Problem

IEC 61000 and IEEE 519 define dozens of thresholds across harmonics, flicker, and voltage deviation. Encoding these as a flexible rules engine — rather than hardcoding checks — meant we could support new standards without rewriting the UI layer.

02

Waveform Rendering at Scale Needs Careful Optimization

Rendering raw harmonic waveform data for hundreds of panels in real time caused visible frame drops. I learned that downsampling on the backend and using canvas-based rendering instead of SVG was critical for keeping the dashboard responsive.

03

False Positives Erode Trust in Compliance Alerts

Early versions flagged transient voltage sags as non-compliance, causing operators to distrust the system. Adding time-windowed averaging before triggering an alert — instead of reacting to instantaneous readings — made alerts meaningful and actionable.

04

Certifiable Accuracy Requires Rigorous Testing

Because compliance results can be used in formal reporting, every calculation had to be validated against certified reference datasets, not just visually 'look right.' Building an automated test suite against known-good values caught calculation errors long before they reached production.

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