ECE 4160 Test 2

Ability to comprehend, to understand and profit from experience, to interpret intelligence, having the capacity for thought and reason; creativity, skill, consciousness, emotion, and intuition

computational models and tools of intelligence; capable of taking large raw numerical sensory data directly, processing them by exploiting the representational parallelism and pipelining the problem, generating reliable and timely responses, and withstanding high fault tolerance

Immune systems, swarm intelligence, fuzzy systems, evolutionary computing, and neural networks

the property of a system whereby the collective behaviors of unsophisticated agents interacting locally with their environment cause coherent functional global patterns to emerge

proximity principle, quality principle, diversity principle, stability principle, adaptability principle

the population should be able to carry out simple space and time computations

the population should be able to respond to quality factors in the environment

the population should not commit its activities along excessively narrow channels

the population should not change its mode of behavior every time the environment changes

the population must be able to change behavior mode when it's worth the computational price

the particles do not take into account all of the other particles but only their neighbors. This corresponds to a limited perception of the outside world

results in the entire swarm particles trying to converge on one spot

system initially has a population of random solutions called particles; each particle has random velocity and memory that keeps track of previous best position and corresponding fitness

approximate reasoning with uncertain facts to infer new facts, with a degree of certainty associated with each fact; modeling common sense

massively parallel distributed processor made up of simple processing units, which has the natural propensity for storing experiential knowledge and making it available for use

Knowledge is acquired by the network from its environment through a learning process; Interneuron connection strengths, known as synaptic weights, are used to store acquired knowledge

ability to cope with a large number of variables in parallel, in real time, in a noisy nonlinear and non-stationary environment

supervised, unsupervised, and reinforcement

biologically inspired models for immunization of engineering systems; to detect and eliminate non‐self materials, called antigens such as virus or cancer cells

the act of finding the best result under given circumstances; the process of finding the conditions that give the maximum or minimum value of a function

a branch of mathematics concerned with the application of scientific methods and techniques to decision making problems and with establishing the best or optimal solutions

DR management to maximize incentives, EMS, optimal power flow, forecasting models, Big Data

Find X = {x1, x2, ..., xn} which minimizes f(X) Where X is an n-dimensional vector called the design vector, f(X) is termed the objective function

Find X = {x1, x2, ..., xn} which minimizes f(X) Where X is an n-dimensional vector called the design vector, f(X) is termed the objective function

The criterion with respect to which the designs is optimized, when expressed as a function of the design variables

When an optimization problem modeling a physical system involves only one objective function

When an optimization problem involves more than one objective function, the task of finding one or more optimum solutions; f(X)=a1f1(X)+a2f2(X)

existence of constraints, nature of the design variables, physical structure of the problem, nature of the equations involved, permissible values of the design variables, deterministic nature of the variables, separability of the functions, objective functions

a mathematical programming problem involving a number of stages, where each stage evolves from the preceding stage in a prescribed manner; defined by control variables and state variables

Highly nonlinear multivariate systems with many constraints represent some of the most difficult systems to control optimally (e.g. electric power grid, smart grid, robot manipulators); provides a computational technique to apply the principle of optimality to sequences of decisions which define an optimal control policy or trajectory

the computational burden caused by computational requirements being proportional to the number of discretization points which grow exponentially

provide an approach to the optimal control and dynamic optimization of nonlinear systems whereby future rewards are estimated based upon state‐action transitions

1) Explicit targets for system outputs are provided at every step. • 2) Explicit differentiable cost as a function of system variables is provided at every step. • 3) A graded cost is provided at each step but explicit relationship with system states is not given. 4) Ungraded reinforcement is provided when appropriate, e.g. binary outcome at the end of a game.

determines optimal control laws for a system by successively adapting two networks, namely an Action network (which dispenses control signals) and a Critic network (which learns the desired performance index for some function associated with the performance index).

adapts a Critic network whose output is an approximation of J(R(t)). The temporal difference method of Barto, Sutton and Anderson turns out to be a special case of HDP

adapts a Critic network whose outputs represent the derivatives of J(R(t))

adapts a Critic network whose output is an approximation of J(R(t)), but adapts it so as to minimize errors in the implied derivatives of J (R(t)) (as well as J(R(t)) itself, with some weighting). GDHP tries to combine the best of HDP and DHP

MIMO system, nonlinear system, model uncertainties, random disturbance, learns over time, adaptive, robust

The diversity in type and quality of information about a decision problem calls for methods and techniques that can assist in information processing

Evaluate the expected monetary value (EMV) from the profit and loss data, and their associated probabilities; Draw the decision flow tree which should yield a best decision based on the highest EMV and/or the lowest expected loss

Consider the possibilities of sampling and accurate information and re-evaluate the new EMV; Draw a new decision flow diagram; This should yield a best decision based on the highest EMV and/or the lowest expected loss

what yields the greatest utility (or maximizes the occurrence of favorable outcomes) no matter how narrow the margin of improvement

a multi-criteria decision-making approach; a decision support tool which can be used to solve complex decision problems; It uses a multi-level hierarchical structure of objectives, criteria, subcriteria, and alternatives

both

planning, resource allocation, conflict resolution

a nonlinear framework for carrying out both deductive and inductive thinking; without the use of the syllogism by considering several factors simultaneously, • allowing for dependence and feedback, and • making numerical tradeoffs to arrive at a synthesis or a conclusion

decision system (DA/AHP), static (dynamic programming), adaptive dynamic programming (DHP/HDP), swarm and evolutionary computation (PSO/Heuristic programming), soft computing/Computational Intelligence (NNs/Fuzzy logic)

Wide‐area situational awareness and intelligence, Outage restoration, load characterization, special protection schemes and islanding

collect phasor data across an area as wide as an entire interconnection, which could be 100s of miles in size, in real-time and display it for operators to understand grid conditions

operators can use synchrophasor data to bring equipment and load back into service without risking power instability or without experiencing unsuccessful reclosing attempts that prolong outages

synchrophasor-based situational awareness can warn operators when the stress is increasing

desktop computing, embedded processors, power PCs, GPUs, high processing computing, RTPIS lab

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

analysis, capture, curation, search, sharing, storage, transfer, visualization, and privacy violations

Volume, Variety, Velocity, Veracity, Variability

Integrate historical and real-time data to implement near-future situational awareness

predictions

models are the heart and longs of advanced analytics; it is a science and art to develop a model

To handle an evolving, uncertain, variable and complex power system - three strands of thinking are needed for • Sense-making • Decision-making • Adaptation; In the center of all these strands exist a 'real-time wealth of knowledge' • Continuous refinement • Learns and unlearns

Co-existence of CSTMs (distributed) is essential for smart grid operations • Harmony • Coordination • Communication

generally consists computational units connected to each other in an ordered distributed manner; suited to model complex systems with temporal and spatial dynamics

Economic drivers, carbon reduction, regulatory compliance, and an increase in the drive to provide residential, commercial, and industrial customer self-management of energy costs and consumption

Extract - Reading the data from a data source that could be in a variety of formats, including relational or raw data; Transform - Converting the extracted data from its current form into the form of the target database; Load - Writing the data into the target data warehouse

an open-source framework that allows for the distributed processing of large data sets across clusters of computers; and the "Swiss Army knife of the 21st century"—that support the ability to process, manage, and give users the ability to directly consume data without moving it around; Scalable, cost-effective, flexible and fault-tolerant

Collection, Management, Analysis, Action

visualization (presentation), situation awareness (decision making), predictive forecasting (prescriptive decision options)

improve the reliability and resiliency of electric grid, optimize the asset management and operations costs, share the data/intelligence for improved decision making, integrate legacy systems for improved data flow, improved data analytics and enterprise intelligence

The significant expertise deficit related to big data management, analytics, and data science; Data scientists not only need to know how to data wrangle, they must also know how to operate a variety of tools on a variety of platforms fed with vast amounts of varied data

staff typically manages the transactional side of the enterprise: billing, accounting, asset management, human resources, and customer records

manages the distribution operations, monitors infrastructure and control center-based systems, and oversees a lot of nonhuman inter- action between systems on the grid

uninformed or poor decision-making, lack of compliance, poor communications, inefficient field operations, inability to effectively report to external stakeholders

descriptive analytics, predictive analytics, and prescriptive analytics

Advanced Metering Infrastructure (AMI), Meter Data Management Systems (MDMS), Outage Management Systems (OMS), Distribution Management Systems (DMS), Enterprise Asset Management Systems (EAS)

Enterprise analytics, grid operations analytics, and consumer analytics

customer satisfaction, reliability, operational efficiency, safety

identify gaps in existing assets and establish sound asset management practices and programs (Mind-the-Gap)

Big data, communication, environment, sustainability, restoration, system scalability, system dynamic, business model change, IT architecture and services, cyber-security, workforce optimization

allows users to take benefit from all these technologies without the need for deep knowledge about or expertise with each one of them; virtualization that separates a physical computing device into one or more "virtual" devices, each of which can be easily used and managed to perform computing tasks

More secure, focused cybersecurity, data privacy, flexible management for utilities, utilities can deploy data analytics applications for rapidly growing data volumes in a secure and scalable manner

telemetry, oscillographic, consumption data, asynchronous event messages, metadata

continuous flow measurements of grid equipment parameters and other grid variables

data made up of voltage and current waveform samples that can creat a graphical record

This is the most often smart meter data, but any node that measures usage data may be included

Grid devices with embedded processors generating messages under a variety of conditions, both as responses and commands

Any data that is used to describe other data

Time it takes for a bit to travel from its sender to its receiver; very important in building a data analytic architecture

operations, energy trading, RT DR, asset management

hierarchical to distributed, multiple data classes, latencies

data processing, analytics, correlations and rules, visualization engine, visualization

Designing intuitive systems that users can operate with a minimum of cognitive friction is the goal of user-interface designers who realize the stakes are high

real-time grid data, predictive analytics, prescriptive analytics, operational response, planning models

Communications, advanced components, automated control systems, sensing and measurement, decision support, customer-facing systems

provide an accessible way to see and understand trends, outliers, and patterns in data

line charts, area charts, and bar charts with time on the x-axis and the measure on the y-axis

bar charts and pie charts; they encode quantitative values as length/size, making is easy to compare values

scatter plots, combine two line charts with a bar chart

boxplots, histograms

maps

Purpose, Choosing the right chart type, create effective views

limit number of colors and shapes in a single view, put most important data on the X or Y axis and less important data on color, size, or shape, avoid overloading your views

dashboards, maps, pie charts, meters, line charts, area charts, bar charts

High Charts, Plotly, Tableau, D3, Vega & Vega-Lite

the dynamics of wind speed, direction, wind turbine type, generator type, power converter, and their associated controls

mechanical power control such as pitch control or stall regulation; fixed speed, variable speed and full power electronic conversion based on speed control

due to complex function approximations between wind speed, wind direction and associated turbine-generator dynamics and controls

Power base is 2 MW; speed base is 1800 rev/min

type A (constant speed WT), type B (variable speed WT), type C (variable speed WT with partial-scale frequency converter), type D (variable speed WT with full-scale frequency converters)

10MW generator and power converter; The SG output voltage magnitude and frequency varies continuously but is coupled to the point of common coupling (PCC) through a dc link voltage source converter.

Tesla

Regulation, spinning reserve, load leveling, storage for renewable sources, demand-response

Cost, battery size/performance, durability, safety

Overloaded, lacking new investments, impediment to rapid deployment of plug-in vehicles on the road

Reserve margin and load forecasting, stability (with rapid load fluctuations)

Takes wind power and power reserve via smart park and interfaces it with the grid

If the difference between available wind power and the demand is negative big and the overall state of charge is medium then the pitch control reference is very high and the SmartPark power command is positive (discharging) big.

Power loss due to dip

Maximize profit for vehicles in a SmartPark by scheduling grid transactions based on price curves.
• Find a suitable good solution much quicker than simply trying every possible combination.
• Use an algorithm that is scalable as the number of vehicles, time steps, and constraints used are increased.