Mohammed Abdallatif
Data Scientist & Energy Consultant
Experience
Data Scientist & Energy Consultant
Accenture GmbH
Data Scientist & Energy Consultant
umlaut energy GmbH
Research Assistant
IPL- TU Dortmund
Predictive Risk Analytics for a Distribution System Operator
Distribution System Operator
Developed a sophisticated LSTM deep learning algorithm to forecast risks for critical assets belonging to a Distribution System Operator.
The project enabled a shift from reactive to predictive maintenance, significantly reducing asset downtime, allowing the staff of the customer sufficient time to take countermeasures ahead of a predicted risk.
- Feature engineering, sensor data collection preprocessing and modeling (energy data, weather data)
- Python/PyTorch (LSTM, Scikit-Learn)
Frequency Stability Simulation GUI for European Grid
European Grid Operator
Development of a custom Python-based graphical user interface (GUI) to simulate and analyze frequency instabilities during system splits in the European transmission network under various generation and load scenarios.
Enhanced grid operation readiness by developing an interactive simulation platform for visualizing the frequency response of separated grid areas. It enabled engineering teams to model various grid separation scenarios, identify vulnerable configurations, and assess the dynamic impact on system frequency.
- Python (wx. for GUI)
- Frequency Stability Simulation
- Power System Modeling
- Scenario-Based System Splitting
Deep Learning Assisted Approach for Online Clustering and Controlled Islanding of Transmission Power Systems
Information Processing Lab, TU Dortmund
Development of a real-time, data-driven framework for controlled islanding (CI) in power systems to mitigate wide-area blackouts. The solution integrates system monitoring, online stability assessment, and generation of optimized islanding strategies using constrained spectral clustering and deep learning assistance.
The research and development, supported by corresponding publications, addressed the complex objective of intentional controlled islanding to prevent or mitigate wide-area blackouts. The approach effectiveness has been shown through dynamic RMS simulation and results evaluation to enable fast and reliable partitioning of power systems under severe disturbances. Delivered accurate online stability monitoring, reduced computation time and supported real-time operator decision-making through ranked split strategy recommendations.
- Controlled Islanding
- Deep Learning
- Constrained Spectral Clustering and k-Harmonic Means Clustering
- Power System Stability Indicators
- Machine Learning-based Ranking
- Generator Coherency Detection
- Co-Simulation: Python (API), DIgSILENT PowerFactory, Matlab
Hydrogen Station Market Analysis and KPI Visualization
Operator of Hydrogen Stations in Germany
Analysis of data sets of hydrogen refueling stations across Germany and built an interactive Power BI dashboard to visualize station distribution, utilization trends, and geographic coverage. Data modeling was employed to structure key market indicators and derive actionable business insights.
Delivering a high-impact dashboard that visualized national hydrogen infrastructure coverage. Enabled stakeholders to track core KPIs and assess market saturation. The solution provided the potential of using big data compared to existing excel sheets, allowing a flexible and extendible business structure.
- Power BI
- Data Modeling and Integration
- KPI Definition & Visualization
Agile-Based Backend Optimization and Dashboard Development for Scalable Analytics Solutions
Railway sector
Leading the development of interactive dashboards to visualize and improve customer journey for a railway customer. I worked within an agile Scrum team to optimize the underlying SQL data warehouse, ensuring high-quality and consistent data for business users. Tasks included backend optimization, data modeling, quality assurance, and delivering front-end dashboards focused on actionable insights and KPI monitoring.
Enhanced the customer journey by equipping business users with precise KPIs and actionable insights. Improved backend efficiency and data reliability, enabling faster, insight-driven dashboards that powered strategic customer engagement and operational decisions.
- SQL Data Warehousing and Backend Query Optimization
- Data Modeling & Normalization
- Quality Assurance & Data Validation
- Dashboard Development (Qlik Sense)
- Agile/Scrum Workflows
Dynamic Security Assessment in the Control Room of a Transmission System Operator
Transmission System Operator
Supported the customer in defining requirements and performing in-depth analysis for the development of a next-generation software system designed to enable dynamic power system security assessments. The tool leverages SCADA data provided in CIM profiles and integrates dynamic power grid models to assess system behavior in real time.
Contributed to the creation of comprehensive requirements documentation, particularly related to the detection and handling of grid violations, ensuring alignment with both national and European regulatory standards. Activities included validation of model data, identification of modeling errors, coordination with key stakeholders, and execution of testing and vulnerability assessments. The project strengthened the foundation for real-time dynamic security operations in the control room environment.
- Voltage Stability Analysis using DIgSILENT PowerFactory and NEPLAN
- Evaluation and validation of grid models based on CIM profiles, with compliance checks against CGMES standards
- Design of test strategies and execution of Site Acceptance Tests (SAT)
Summary
I am a Senior Data Scientist and Project Manager at the intersection of data science and energy and power grid operators. I develop innovative, data-driven solutions for the energy, rail, and public sectors.
I am a results-driven senior data scientist and certified project manager. My background includes conducting academic research at the intersection of data science and power systems, as well as delivering impactful industry solutions in consulting, software development, and project management domains for clients in the energy and public sectors.
I specialize in leading and executing complex end-to-end (E2E) technical projects, transforming data into actionable, high-value solutions. My expertise spans advanced data science, cloud computing, and AI, grounded in extensive experience in the energy, power systems, and public sectors. I have successfully delivered value-driven solutions to a broad range of clients, including power plant operators, distribution and transmission system operators (DSOs & TSOs), railway operators and public sector institutions.
Languages
Education
TU Dortmund
PhD (cand.) · Machine Learning Assisted Energy System Operations · Dortmund, Germany
TU Dortmund
M.Sc. · Electrical Engineering And Information Technologies · Dortmund, Germany
TU Dortmund
B.Sc. · Electrical Engineering And Information Technologies · Dortmund, Germany
Certifications & licenses
Azure Fundamentals – AZ900
Microsoft
Certified Product Owner – PSPO I
Scrum.org
Project Management Professional – PMP
PMI
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