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Smart Grid Exam 2

Computational Intelligence and IoT

TermDefinition
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
Created by: zbowers
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