Energy Loss Management for Distribution Systems

Energy Loss within Distribution Networks is an important as well as a critical issue that Energy regulatory bodies are generally concerned about especially on how it directly impacts users in terms of costs. The reduction in Technical and non-technical losses, ensures the the supplier’s costs is reduced as less electricity is used from the power generation company. This in turn reduces the cost of electricity supply for customers. Utility engineers need to understand the cause of the losses, the methods for estimating the losses, and how to reduce the losses in a cost-effective manner. This course will provide the knowledge and calculation methods for estimating losses, and discuss techniques to reduce potential losses. If you aim maybe to master the processes of being an Energy Loss Expert or broaden you knowledge in the field, this programme caters for that as well.

1. Sources of load losses
2. Sources of no load losses
3. Estimation techniques
4. Load allocation issues
5. Loss reduction techniques
6. Cost effectiveness of loss reduction
7. Transformer cost of losses formula

The course is intended for Distribution Engineers

• Transmission Engineers
• Regulators
• Production Engineers
• Power Quality
• Quality Assurance Engineers
• Consultants
• Large commercial and manufacturing electricity users

Topic 1: Brief Introduction of Data-Driven Analytics in Smart Electric Power Distribution Systems

• 1.1 Introduction to data-driven analytics
• 1.2 Data Volume, Variety, Velocity, and Value
• 1.3 Applications of big data analytics in smart electric power distribution systems

Topic 2: Singular Value Decomposition (SVD)

• 2.1 Application of SVD in Power System Anomaly Detection
• 2.2 Application of SVD in Bad Data Processing for State Estimation

Topic 3: Data-driven Application One: Distribution System Topology Identification

• 3.1 Introduction to machine learning algorithms
• 3.2 Unsupervised machine learning algorithm for phase identification
• 3.3 Supervised machine learning algorithm for phase identification
• 3.4 Transformer to customer association identification
• 3.5 Case study: Phase connectivity identification

Topic 4 Data-driven Application Two: Electricity Theft Detection

• 4.1 Development of modified linear model for distribution secondary
• 4.2 Physically inspired electricity theft detection method
• 4.3 Anomaly score and visualization of electricity theft

Topic 5 Data-driven Application Three: Evaluating the effectiveness of CVR

• 5.1 Introduction to robust regression
• 5.2 Quantify the load and voltage impacts of Conservation Voltage Reduction

Topic 6: Demand Response Forecast

• 6.1 Introduction to the mixture model
• 6.2 Forecast the load impact from demand response resources

Delegates who attend and complete the course would be issued a Certificate of Completion by Camaval International.

For further enquires kindly contact

Course Coordinator via email info@camaval.com

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