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Smart Grid Exam 2
Computational Intelligence and IoT
Term | Definition |
---|---|
Parts of a Smart Grid | Computational Intelligence Intelligent Measurements Intelligent Communication Intelligent Power Electronics Intelligent Decisions and Control Renewable Resources Cyber Security Visual and Data Analytics |
6 V's | Value Variety Variability Velocity Veracity Volume |
4 Parts of Hadoop | Common Distributed File System YARN MapReduce |
SDN | Software Defined Networking |
Modeling, optimization, and control capabilities needed for SG operation: | Grid Mapping Demand Response Control Load Shifting/Shedding Outage Mapping Descriptive Modeling Diagnostic Modeling Predictive Modeling Prescriptive Modeling |
AHP | Analytic Hierarchy Process - A multi-criteria decision making approach which can be used to solve complex decision problems. |
Computations needed in the case of smart grid | Predictive Analytical Faster Than Real-Time |
Special features of a smart grid compared to the legacy systems | Data Collection Cyber-Security |
Big Data | An all-encompassing term for any collection of data sets so large and complex that it becomes difficult to process using traditional data processing applications. |
Challenges to Big Data | Analysis Capture Curation Search Sharing Storage Transfer Visualization Privacy Violations |
Three biggest areas of Big Data according to EPRI | Visualization Situational Awareness Predictive forcasting |
5 Benefits of Big Data | Improve reliability&resiliency of the electric grid Optimize asset management&operations costs Share the data/intelligence for improved decision making Integrate legacy systems from improved data flow Improved data analytics & enterprise Intelligence |
3 analytical subsets often used within the utility industry | Descriptive Predictive Prescriptive |
Virtualization | The enabling technology for cloud computing |
5 classes of utility data | Telemetry Oscillographic Consumption Data Asynchronous Event Messages Metadata |
5 areas of cyber-security analytics | Communications Advanced components Automated Control Systems Sensing and Measurements Decision Support Customer-Facing Systems |
5 paradigms of Computational Intelligence | Immune Systems Neural Networks Swarm Intelligence Fuzzy Systems Evolutionary Computing |
Swarm Intelligence | Multiple individual agents working with their environments as a collective to form logical and consistent global patterns |
5 Principles of Swarm Intelligence | Proximity - Simple space time calc. Quality - Respond to qual factors. Diversity - No actions along narrow chan. Stability - No changing ever time environ chang. Adaptability - Change when worth the price. |
Computational Intelligence | Taking large data inputs. Processing data by exploiting the representative parallelism and pipeline the problem. Reliable and timely response. High-fault tolerance. Robust and Adaptive. |
Reinforcement Learning | Correct actions are rewarded, incorrect actions are penalized. |
Two types of ACDs | Action Dependent - Critic is directly connected to the action. Model Dependent - Model is between the action and the critic. |
Adaptive Critical Designs give brain like intelligence for | MIMO Nonlinear Model Uncertainties Random Disturbances Learns over time Adaptive Robustness |
Applications of AHP | Planning Resource Allocation Conflict Resolution |