These are the slides for my keynote lecture "AI Techniques for Smart Grids" at the 2014 IEEE Innovative Smart Grid Technologies - Asia conference where I discussed the role and potential of self-organization in the smart grid.
The smart grid is an electrical power grid that is integrated with an AI enabled, two way communication network providing energy and information. It is a technology that enables instantaneous feedback from various sensors and devices on the operation of the power grid. Although AI is relatively new, it is poised to revolutionize the way we produce, transmit, and consume energy. AI will constitute the brain of future smart grid. The power sector has started to use AI and related technologies for communication between smart grids, smart meters, and Internet of things devices. This paper presents some applications of AI in smart grid. Matthew N. O. Sadiku | Uwakwe C. Chukwu | Abayomi Ajayi-Majebi | Sarhan M. Musa "Artificial Intelligence in Smart Grid" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-5 , August 2022, URL: https://www.ijtsrd.com/papers/ijtsrd50563.pdf Paper URL: https://www.ijtsrd.com/engineering/other/50563/artificial-intelligence-in-smart-grid/matthew-n-o-sadiku
Cybersecurity for Smart Grids: Vulnerabilities and Strategies to Provide Cybe...Leonardo ENERGY
This Cybersecurity webinar addresses issues of importance to executive, technical, and academic professionals involved with managing and protecting Electric Utilities and Smart Grids. Cyber threats and vulnerabilities, including cyber attacks, will be addressed; as well as Smart Grid trends, and privacy and data integrity issues. United States, European, and International organizations and initiatives to address cybersecurity for utilities will be discussed. The webinar will conclude with strategies to improve cybersecurity. A second cybersecurity webinar (programmed in September 2017) will address best practices, case studies, and legal and regulatory constraints for architecting smart grids in a secure way.
This document discusses wide area monitoring systems (WAMS) and their components. WAMS use phasor measurement units (PMU) synchronized by GPS to measure voltage and currents across large areas of the power grid. A phasor data concentrator (PDC) collects PMU data and performs monitoring, alarming, event triggering, and quality checks. WAMS allow real-time monitoring of grid dynamics to detect and prevent instability issues, providing benefits over traditional SCADA systems with slower sampling. The document reviews several WAMS implementations at utilities in countries like Finland, Switzerland, Croatia, Austria, and Thailand.
Artificial intelligence in power stationsMATHEW JOSEPH
Artificial intelligence is being used in power stations to help ensure a reliable power supply. New digital technologies allow for intelligent substations using information technology and high-speed communication systems. These advanced monitoring and control systems improve performance, share information, and integrate power distribution management. Adopting these artificial intelligence applications can reduce costs through remote surveillance, integrated equipment management, and space savings from miniaturized equipment.
The document discusses India's power grid network and the transition to a smart grid system. It provides information on:
- India's existing regional power grids and their interconnections.
- The definition and key characteristics of a smart grid, including its use of digital technology, smart meters, and two-way communication.
- The advantages of a smart grid like enabling renewable energy integration, demand response programs, and modernizing transmission and distribution systems.
1) Traditional electromechanical meters have issues like drift over time and temperature that digital smart meters improve on. Smart meters allow for automated and remote reading to improve efficiency.
2) Advanced Metering Infrastructure involves integrating smart meters, communication networks, and data management systems to allow two-way communication between utilities and customers. This enables features like time-of-use pricing and remote service disconnects.
3) Key components of AMI include smart meters, wide area communication networks, home area networks connected to devices, and meter data management systems to aggregate and analyze usage data.
Artificial intelligence in power systems Riyas K H
1) Artificial intelligence techniques like artificial neural networks, fuzzy logic systems, and expert systems can be applied to problems in power systems.
2) Artificial neural networks are useful for tasks like power system stabilization, load forecasting, fault diagnosis, and security assessment. They do not require extensive programming.
3) Fuzzy logic systems account for measurement errors and are used for stability assessment, fault diagnosis, and other applications. Expert systems use rules and facts to deduce conclusions.
Artificial intelligence in power systemBittu Goswami
This document discusses the use of artificial intelligence techniques like expert systems, artificial neural networks, and fuzzy logic in power systems. It provides an overview of each technique, their advantages and disadvantages, and examples of how they can be applied. Specifically, it describes how expert systems can be used for transmission line parameter estimation, and how neural networks and fuzzy logic can be applied to fault detection and diagnosis to improve system reliability and efficiency. The document concludes that while AI is increasingly being used in power systems, further research is still needed to fully realize its benefits.
The smart grid is an electrical power grid that is integrated with an AI enabled, two way communication network providing energy and information. It is a technology that enables instantaneous feedback from various sensors and devices on the operation of the power grid. Although AI is relatively new, it is poised to revolutionize the way we produce, transmit, and consume energy. AI will constitute the brain of future smart grid. The power sector has started to use AI and related technologies for communication between smart grids, smart meters, and Internet of things devices. This paper presents some applications of AI in smart grid. Matthew N. O. Sadiku | Uwakwe C. Chukwu | Abayomi Ajayi-Majebi | Sarhan M. Musa "Artificial Intelligence in Smart Grid" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-5 , August 2022, URL: https://www.ijtsrd.com/papers/ijtsrd50563.pdf Paper URL: https://www.ijtsrd.com/engineering/other/50563/artificial-intelligence-in-smart-grid/matthew-n-o-sadiku
Cybersecurity for Smart Grids: Vulnerabilities and Strategies to Provide Cybe...Leonardo ENERGY
This Cybersecurity webinar addresses issues of importance to executive, technical, and academic professionals involved with managing and protecting Electric Utilities and Smart Grids. Cyber threats and vulnerabilities, including cyber attacks, will be addressed; as well as Smart Grid trends, and privacy and data integrity issues. United States, European, and International organizations and initiatives to address cybersecurity for utilities will be discussed. The webinar will conclude with strategies to improve cybersecurity. A second cybersecurity webinar (programmed in September 2017) will address best practices, case studies, and legal and regulatory constraints for architecting smart grids in a secure way.
This document discusses wide area monitoring systems (WAMS) and their components. WAMS use phasor measurement units (PMU) synchronized by GPS to measure voltage and currents across large areas of the power grid. A phasor data concentrator (PDC) collects PMU data and performs monitoring, alarming, event triggering, and quality checks. WAMS allow real-time monitoring of grid dynamics to detect and prevent instability issues, providing benefits over traditional SCADA systems with slower sampling. The document reviews several WAMS implementations at utilities in countries like Finland, Switzerland, Croatia, Austria, and Thailand.
Artificial intelligence in power stationsMATHEW JOSEPH
Artificial intelligence is being used in power stations to help ensure a reliable power supply. New digital technologies allow for intelligent substations using information technology and high-speed communication systems. These advanced monitoring and control systems improve performance, share information, and integrate power distribution management. Adopting these artificial intelligence applications can reduce costs through remote surveillance, integrated equipment management, and space savings from miniaturized equipment.
The document discusses India's power grid network and the transition to a smart grid system. It provides information on:
- India's existing regional power grids and their interconnections.
- The definition and key characteristics of a smart grid, including its use of digital technology, smart meters, and two-way communication.
- The advantages of a smart grid like enabling renewable energy integration, demand response programs, and modernizing transmission and distribution systems.
1) Traditional electromechanical meters have issues like drift over time and temperature that digital smart meters improve on. Smart meters allow for automated and remote reading to improve efficiency.
2) Advanced Metering Infrastructure involves integrating smart meters, communication networks, and data management systems to allow two-way communication between utilities and customers. This enables features like time-of-use pricing and remote service disconnects.
3) Key components of AMI include smart meters, wide area communication networks, home area networks connected to devices, and meter data management systems to aggregate and analyze usage data.
Artificial intelligence in power systems Riyas K H
1) Artificial intelligence techniques like artificial neural networks, fuzzy logic systems, and expert systems can be applied to problems in power systems.
2) Artificial neural networks are useful for tasks like power system stabilization, load forecasting, fault diagnosis, and security assessment. They do not require extensive programming.
3) Fuzzy logic systems account for measurement errors and are used for stability assessment, fault diagnosis, and other applications. Expert systems use rules and facts to deduce conclusions.
Artificial intelligence in power systemBittu Goswami
This document discusses the use of artificial intelligence techniques like expert systems, artificial neural networks, and fuzzy logic in power systems. It provides an overview of each technique, their advantages and disadvantages, and examples of how they can be applied. Specifically, it describes how expert systems can be used for transmission line parameter estimation, and how neural networks and fuzzy logic can be applied to fault detection and diagnosis to improve system reliability and efficiency. The document concludes that while AI is increasingly being used in power systems, further research is still needed to fully realize its benefits.
The document discusses smart meters and the smart grid. It defines the electric grid and how smart grids modernize it using communication technologies. Smart meters are two-way communicating electric meters that provide more detailed energy usage data to utilities in real-time. They are different than conventional meters by being bi-directional and able to connect to home networks and the smart grid. The benefits of smart meters include more accurate billing, outage detection, load management capabilities, and energy savings.
In today's era of advanced technology, Artificial Intelligence has been proven as a boon for various fields. Utilization of AI in power system is the need of upcoming future.
A smart meter is an electronic device
that records information such as
consumption of electric energy, voltage
levels, current, and power factor. Smart
meters communicate the information to
the consumer for greater clarity of
consumption behavior, and electricity
suppliers for system monitoring and
customer billing.
This document discusses the cyber security risks of smart grids and proposes an integrated security framework to address these risks. Smart grids integrate information infrastructure with electrical infrastructure, improving performance but also increasing vulnerability to cyber attacks. The framework features security agents, managed security switches, and a security manager to provide layered protection, intrusion detection, and access control across the power automation network in a scalable and extensible manner. This integrated approach is needed as power systems have different security needs than traditional IT networks.
This document discusses issues related to interconnecting microgrids. It describes how a DC microgrid system utilizes a DC bus to distribute power from photovoltaic units and battery storage to local households. Interconnection can be done directly through switchgear or power electronic interfaces. Key issues that can arise include voltage and frequency fluctuations that occur due to imbalance between supply and demand, power factor correction needs, and harmonics produced by some loads. Unintentional islanding is also a safety concern that must be addressed when connecting microgrids to the main power grid.
advanced metering infrastructure, advanced meter reading, internet of Things, WiMax, LTE, smart meter analytics, smart meter communication technologies, LTE advanced, WiFi, smart meter architectural blueprint
More details: (blog: http://sandyclassic.wordpress.com ,
linkedin: ie.linkedin.com/in/sandepsharma/)
A Smart Grid is an electrical grid that uses information and communications technology to gather and act on information, such as information about the behaviors of suppliers and consumers, in an automated fashion to improve the efficiency, reliability, economics, and sustainability of electricity production and distribution. Just as ICs were used to improve the bandwidth of copper cable, they can also be used to improve the bandwidth of electrical cables. These improvements enable a Smart Grid to more effectively purchase and distribute electricity and provide users with real-time prices including time of day prices.
The document describes a syllabus for a course on smart grid technologies. It covers four modules: introduction to smart grids; information and communication technologies for smart grids; sensing, measurement, control and automation; and power electronics and energy storage. It provides details on the topics that will be covered in each module, such as smart metering and demand-side integration. The goal is for students to gain a clear understanding of smart grid technologies to enable research in the area.
This document discusses smart grids and was presented by Norrazman Zaiha Zainol. It outlines that smart grids use digital technologies to create two-way communication between electricity suppliers, distributors, and consumers. This allows demand to be optimized and renewable energy to be integrated. The key components of smart grids include centralized generation facilities, transmission infrastructure, end-user technologies, and physical and software networks to connect all parts of the system. Smart grids provide benefits like enabling consumer participation, optimizing asset usage, and integrating intermittent renewable sources, but also face challenges regarding data privacy, fair distribution of demand, and ensuring system security.
Airtificial Intelligence in Power SystemPratik Doshi
SCADA (Supervisory Control and Data Acquisition) systems are used to monitor and control infrastructure like power grids, water treatment plants, and oil and gas pipelines. A typical SCADA system includes remote terminal units that collect data from sensors and control equipment in the field, a communication system to transmit data to a central control room, and human-machine interfaces that allow operators to monitor and manage the infrastructure. SCADA systems improve efficiency by automating monitoring and control functions while also enhancing reliability through features like remote access, data logging, and alarm notifications.
This document discusses monitoring in smart power grids using phasor measurement units (PMUs). It describes how PMUs provide real-time measurements that allow monitoring of key phenomena like islanding detection, line thermal monitoring, power system stability, and out-of-step stability. Monitoring is important for power assurance, visibility, efficiency and planning. PMU data supports applications like real-time monitoring, protection, and control and allows detection of oscillations and instability that could lead to blackouts. The conclusion emphasizes that modern monitoring delivers confidence in power system performance and ability to predict and prevent problems.
The document discusses the use of artificial intelligence techniques in power systems. It describes how AI can help address challenges from the complex, large amounts of data in power systems. The major AI techniques that can be applied include expert systems, artificial neural networks, and fuzzy logic. These techniques have advantages like consistent processing speed but also disadvantages like inability to learn new problems. The document provides examples of applications for fault diagnosis, load forecasting, stability analysis and more. It concludes that AI can improve reliability and reduce costs but more research is still needed to realize its full benefits.
IoT Solutions for Smart Energy Smart Grid and Smart Utility ApplicationsEurotech
Smart Energy Smart Grid and Smart Infrastructure - Many Applications and Devices
An introduction to Eurotech' s IoT Field-to-Application Building Blocks for the Energy and Utility Industry
This document discusses smart grid technology in India. It begins with an introduction to smart grids and the current one-way electricity transmission system. It then discusses India's increasing electricity needs and deficits. The main points are:
- A smart grid uses communication technology to collect data from suppliers and consumers to automate distribution management.
- Smart grids have two-way interaction and include components like smart meters, distributed generation, and information transfer.
- Smart grids can help reduce carbon footprints, improve efficiency, enable self-healing of outages, and increase use of renewable energy through technologies like smart meters and distributed generation.
This document provides an overview of artificial intelligence techniques and their applications in power systems. It discusses expert systems, artificial neural networks, and fuzzy logic systems as the three major AI techniques used. It describes how each technique works and its advantages/disadvantages. The document also gives examples of how these techniques can be applied in transmission lines, power system protection, and other areas like operations, planning, control, and automation of power systems. The conclusion states that while AI shows promise for improving power system efficiency and reliability, more research is still needed to fully realize its benefits.
Smart Grid: Definition
• Need of smart grid
• Smart grid functions
• How Smart Grid Works
• Smart Grid: Benefits
• Smart grid components and its Benefits
• Issues and Challenges
• Opportunities in future
• Smart Grid Projects in India and Gujarat
• Question-Answer
• References
Wanted!: Open M&S Standards and Technologies for the Smart Grid - Introducing...Luigi Vanfretti
Title:
Wanted! - Open M&S Standards and Technologies for the Smart Grid
Subtitle:
Introducing the Open Source iTesla Power Systems Modelica Library and the RaPId Toolbox for Model Identification and Validation
Abstract:
Modeling and Simulation (M&S) technologies have a broad set of applications in power systems, from infrastructure planning, through real-time testing of components, and even for training operators to use decision support systems. However, power system M&S technologies face a great challenge to meet when designing, testing, operating and controlling cyber-physical and sustainable electrical energy systems and components, a.k.a “Smart Grids”.
The speaker claims that open M&S standards can have a large role to play in the development of Smart Grids. This claim will be justified with three examples.
The first example describes the experience gained during the EU FP7 iTesla project where the iTesla Power Systems Modelica Library (iPSL) was designed using the Modelica language. The Modelica language, being standardized and equation-based, has proven valuable for the project for model exchange, and even simulation of actual power networks.
Within the iTesla project, the KTH SmarTS Lab research group has been also applying the FMI standard for model exchange in order to develop a software prototype called RaPId. The RaPId Toolbox aims to provide a “virtual laboratory” to solve parameter identification and model validation problems for any kind of model represented in an FMU, but specifically, for power systems.
The third example comes from a collaboration with Xogeny. It will be shown how it is possible to exploit the FMI to decouple the model from the simulator tool, and thus, exploit the model in unforeseen ways. This shows that is possible develop customized and stand-alone analysis tools using web technologies, giving analyst more time for “analysis”. This approach has an enormous potential for typical analysis applications, but even more, for education.
The RaPId Toolbox for Parameter Identification and Model Validation: How Mode...Luigi Vanfretti
RaPId is a recursive acronym for Rapid Parameter Identification. The toolbox was built within WP3 of the FP7 iTesla project. It uses Modelica models compiled in FMUs compliant with the FMI standard, which are imported into Simulink using the FMI Toolbox for Matlab/Simulink from Modelon. Within the Matlab environment, we have developed a plug-in architecture that lets the user choose many different (or even their own) optimization solvers for parameter calibration. Not to mention, you can choose any simulation solver available in Simulink (not just trapezoidal integration!)
The document discusses smart meters and the smart grid. It defines the electric grid and how smart grids modernize it using communication technologies. Smart meters are two-way communicating electric meters that provide more detailed energy usage data to utilities in real-time. They are different than conventional meters by being bi-directional and able to connect to home networks and the smart grid. The benefits of smart meters include more accurate billing, outage detection, load management capabilities, and energy savings.
In today's era of advanced technology, Artificial Intelligence has been proven as a boon for various fields. Utilization of AI in power system is the need of upcoming future.
A smart meter is an electronic device
that records information such as
consumption of electric energy, voltage
levels, current, and power factor. Smart
meters communicate the information to
the consumer for greater clarity of
consumption behavior, and electricity
suppliers for system monitoring and
customer billing.
This document discusses the cyber security risks of smart grids and proposes an integrated security framework to address these risks. Smart grids integrate information infrastructure with electrical infrastructure, improving performance but also increasing vulnerability to cyber attacks. The framework features security agents, managed security switches, and a security manager to provide layered protection, intrusion detection, and access control across the power automation network in a scalable and extensible manner. This integrated approach is needed as power systems have different security needs than traditional IT networks.
This document discusses issues related to interconnecting microgrids. It describes how a DC microgrid system utilizes a DC bus to distribute power from photovoltaic units and battery storage to local households. Interconnection can be done directly through switchgear or power electronic interfaces. Key issues that can arise include voltage and frequency fluctuations that occur due to imbalance between supply and demand, power factor correction needs, and harmonics produced by some loads. Unintentional islanding is also a safety concern that must be addressed when connecting microgrids to the main power grid.
advanced metering infrastructure, advanced meter reading, internet of Things, WiMax, LTE, smart meter analytics, smart meter communication technologies, LTE advanced, WiFi, smart meter architectural blueprint
More details: (blog: http://sandyclassic.wordpress.com ,
linkedin: ie.linkedin.com/in/sandepsharma/)
A Smart Grid is an electrical grid that uses information and communications technology to gather and act on information, such as information about the behaviors of suppliers and consumers, in an automated fashion to improve the efficiency, reliability, economics, and sustainability of electricity production and distribution. Just as ICs were used to improve the bandwidth of copper cable, they can also be used to improve the bandwidth of electrical cables. These improvements enable a Smart Grid to more effectively purchase and distribute electricity and provide users with real-time prices including time of day prices.
The document describes a syllabus for a course on smart grid technologies. It covers four modules: introduction to smart grids; information and communication technologies for smart grids; sensing, measurement, control and automation; and power electronics and energy storage. It provides details on the topics that will be covered in each module, such as smart metering and demand-side integration. The goal is for students to gain a clear understanding of smart grid technologies to enable research in the area.
This document discusses smart grids and was presented by Norrazman Zaiha Zainol. It outlines that smart grids use digital technologies to create two-way communication between electricity suppliers, distributors, and consumers. This allows demand to be optimized and renewable energy to be integrated. The key components of smart grids include centralized generation facilities, transmission infrastructure, end-user technologies, and physical and software networks to connect all parts of the system. Smart grids provide benefits like enabling consumer participation, optimizing asset usage, and integrating intermittent renewable sources, but also face challenges regarding data privacy, fair distribution of demand, and ensuring system security.
Airtificial Intelligence in Power SystemPratik Doshi
SCADA (Supervisory Control and Data Acquisition) systems are used to monitor and control infrastructure like power grids, water treatment plants, and oil and gas pipelines. A typical SCADA system includes remote terminal units that collect data from sensors and control equipment in the field, a communication system to transmit data to a central control room, and human-machine interfaces that allow operators to monitor and manage the infrastructure. SCADA systems improve efficiency by automating monitoring and control functions while also enhancing reliability through features like remote access, data logging, and alarm notifications.
This document discusses monitoring in smart power grids using phasor measurement units (PMUs). It describes how PMUs provide real-time measurements that allow monitoring of key phenomena like islanding detection, line thermal monitoring, power system stability, and out-of-step stability. Monitoring is important for power assurance, visibility, efficiency and planning. PMU data supports applications like real-time monitoring, protection, and control and allows detection of oscillations and instability that could lead to blackouts. The conclusion emphasizes that modern monitoring delivers confidence in power system performance and ability to predict and prevent problems.
The document discusses the use of artificial intelligence techniques in power systems. It describes how AI can help address challenges from the complex, large amounts of data in power systems. The major AI techniques that can be applied include expert systems, artificial neural networks, and fuzzy logic. These techniques have advantages like consistent processing speed but also disadvantages like inability to learn new problems. The document provides examples of applications for fault diagnosis, load forecasting, stability analysis and more. It concludes that AI can improve reliability and reduce costs but more research is still needed to realize its full benefits.
IoT Solutions for Smart Energy Smart Grid and Smart Utility ApplicationsEurotech
Smart Energy Smart Grid and Smart Infrastructure - Many Applications and Devices
An introduction to Eurotech' s IoT Field-to-Application Building Blocks for the Energy and Utility Industry
This document discusses smart grid technology in India. It begins with an introduction to smart grids and the current one-way electricity transmission system. It then discusses India's increasing electricity needs and deficits. The main points are:
- A smart grid uses communication technology to collect data from suppliers and consumers to automate distribution management.
- Smart grids have two-way interaction and include components like smart meters, distributed generation, and information transfer.
- Smart grids can help reduce carbon footprints, improve efficiency, enable self-healing of outages, and increase use of renewable energy through technologies like smart meters and distributed generation.
This document provides an overview of artificial intelligence techniques and their applications in power systems. It discusses expert systems, artificial neural networks, and fuzzy logic systems as the three major AI techniques used. It describes how each technique works and its advantages/disadvantages. The document also gives examples of how these techniques can be applied in transmission lines, power system protection, and other areas like operations, planning, control, and automation of power systems. The conclusion states that while AI shows promise for improving power system efficiency and reliability, more research is still needed to fully realize its benefits.
Smart Grid: Definition
• Need of smart grid
• Smart grid functions
• How Smart Grid Works
• Smart Grid: Benefits
• Smart grid components and its Benefits
• Issues and Challenges
• Opportunities in future
• Smart Grid Projects in India and Gujarat
• Question-Answer
• References
Wanted!: Open M&S Standards and Technologies for the Smart Grid - Introducing...Luigi Vanfretti
Title:
Wanted! - Open M&S Standards and Technologies for the Smart Grid
Subtitle:
Introducing the Open Source iTesla Power Systems Modelica Library and the RaPId Toolbox for Model Identification and Validation
Abstract:
Modeling and Simulation (M&S) technologies have a broad set of applications in power systems, from infrastructure planning, through real-time testing of components, and even for training operators to use decision support systems. However, power system M&S technologies face a great challenge to meet when designing, testing, operating and controlling cyber-physical and sustainable electrical energy systems and components, a.k.a “Smart Grids”.
The speaker claims that open M&S standards can have a large role to play in the development of Smart Grids. This claim will be justified with three examples.
The first example describes the experience gained during the EU FP7 iTesla project where the iTesla Power Systems Modelica Library (iPSL) was designed using the Modelica language. The Modelica language, being standardized and equation-based, has proven valuable for the project for model exchange, and even simulation of actual power networks.
Within the iTesla project, the KTH SmarTS Lab research group has been also applying the FMI standard for model exchange in order to develop a software prototype called RaPId. The RaPId Toolbox aims to provide a “virtual laboratory” to solve parameter identification and model validation problems for any kind of model represented in an FMU, but specifically, for power systems.
The third example comes from a collaboration with Xogeny. It will be shown how it is possible to exploit the FMI to decouple the model from the simulator tool, and thus, exploit the model in unforeseen ways. This shows that is possible develop customized and stand-alone analysis tools using web technologies, giving analyst more time for “analysis”. This approach has an enormous potential for typical analysis applications, but even more, for education.
The RaPId Toolbox for Parameter Identification and Model Validation: How Mode...Luigi Vanfretti
RaPId is a recursive acronym for Rapid Parameter Identification. The toolbox was built within WP3 of the FP7 iTesla project. It uses Modelica models compiled in FMUs compliant with the FMI standard, which are imported into Simulink using the FMI Toolbox for Matlab/Simulink from Modelon. Within the Matlab environment, we have developed a plug-in architecture that lets the user choose many different (or even their own) optimization solvers for parameter calibration. Not to mention, you can choose any simulation solver available in Simulink (not just trapezoidal integration!)
Embedded System
Embedded systems are integrated system made up of computer hardware and software that performs a specific job. These embedded systems can operate independently or as part of a larger system and may require minimal or no human intervention to function. The use of embedded systems has become increasingly common in a wide range of industries due to their reliability, efficiency, and ability to perform tasks that may be too complex or time-consuming for humans to complete.
53_36765_ME591_2012_1__1_1_Mechatronics System Design.pdfDvbRef1
Mechatronics is a multidisciplinary design approach that integrates mechanical engineering, electrical engineering, computer science, and systems design engineering. It combines sensors and actuators with digital computers and basic control loops to create electromechanical systems. Key elements of mechatronic systems include modeling and simulation, automatic controls, optimization, electrical systems like motors and sensors, actuators, computer systems, and real-time interfacing between physical systems and computational control systems. Mechatronics is used in various applications including automobiles, aircraft, manufacturing machines, and mobile sensor networks.
This document provides an introduction to mechatronic systems. It defines mechatronics as the synergistic integration of mechanics, electronics, controls, and computer engineering towards developing smart products and systems. Mechatronic engineers develop automation solutions to improve quality of life, enhance product quality, and replace manual labor. The document then discusses the history of mechatronics from the industrial revolution to modern information age. It outlines the typical modules in a mechatronic system including sensing, control, and actuation. Examples of mechatronic systems are given such as industrial robots, automotive systems, and mobile robots. The relationship between mechatronics and automation is explored.
Mechatronics originated in 1969 in Japan as the synergistic integration of mechanical engineering with electronics and intelligent computer control in design and manufacturing. It aims to develop embedded distributed computer control systems. Key elements of mechatronics systems include actuators, sensors, signal conditioning, digital logic circuits, software, computers and displays. Common applications include automatic controls in appliances, vehicles, medical devices, and other systems that integrate electrical and mechanical components for increased functionality.
Introduction to Mechatronics Systems- An OverviewFebinSuresh2
1. Mechatronics integrates mechanical engineering, electronics, control engineering, and computer science for designing and manufacturing computer-controlled mechanical systems.
2. A mechatronic system consists of mechanical components, electronic components like sensors and actuators, and control and computing elements. Sensors collect information which is processed by a controller to generate signals for actuators.
3. Control systems can be open-loop or closed-loop. Open-loop systems do not use feedback while closed-loop systems incorporate feedback for greater accuracy and less sensitivity to disturbances.
Functional Blocks of an IoT Ecosystem
The functioning blocks of Internet of Things devices vary based on their complexity and intention. But some of the common usable functioning blocks of IoT devices are Sensors, Processors, Connectivity Modules, Power supply, Memory and storage, User Interface, Security, Actuators, & data Processing & analytics
INTRODUCTION OF SENSORS AND ACTUATORS
Sensors can measure or quantify or respond to the ambient changes in their environment or within the intended zone of their deployment
Generate responses to external stimuli or physical phenomenon through input
functions and their conversion into electrical signals.
It is insensitive to any other property besides what it is designed to detect
A sensor does not influence the measured property
A sensor node
Combination of a sensor or sensors, a processor unit, a radio unit, and a power unit
The nodes are capable of sensing the environment they are set to measure and communicate the information to other sensor nodes or a remote server
Sensing Types
Sensing is divided into 4 categories based on the nature of the environment being sensed and the physical sensors being used to do: 1) scalar sensing, 2) multimedia sensing, 3) hybrid sensing, and 4) virtual sensing
Active or passive:
Sensors can be categorized based on whether they produce an energy output and typically require an external power supply (active).
Whether they simply receive energy and typically require no external power supply (passive).
Invasive or non-invasive:
Sensors can be categorized based on whether a sensor is part of the environment it is measuring (invasive)
External to it (non-invasive).
Contact or no-contact:
Sensors can be categorized based on whether they require physical contact with what they are measuring (contact) or not (no-contact).
Absolute or relative:
Sensors can be categorized based on whether they measure on an absolute scale (absolute) or based on a difference with a fixed or variable reference value (relative).
Categorization based on what physical phenomenon a sensor is measuring
A machine or system’s component that can affect the movement or control the said
mechanism or the system.
Control systems affect changes to the environment or property they are controlling
through actuators.
The system activates the actuator through a control signal, which may be digital or analog.
The outline of a simple actuation system.
Actuators are divided into seven classes:
Hydraulic
Pneumatic
Electrical
Thermal / magnetic
Mechanical
Soft memory polymers
Shape memory polymers.
Hydraulic actuators
Pneumatic actuators
The document discusses various topics related to mechatronics and mechanical systems. It begins by defining mechatronics as the application of electronics and computer technology to control mechanical systems. It then discusses some key aspects of mechatronics like its multidisciplinary approach and concurrent use of electrical, mechanical, control and computer engineering. Examples provided include electronic fuel injection systems replacing mechanical ones. The document also covers topics like sensors, transducers, gears, cams, hydraulic systems and their applications.
Modeling and Simulation of Electrical Power Systems using OpenIPSL.org and Gr...Luigi Vanfretti
Title:
Modeling and Simulation of Electrical Power Systems using OpenIPSL.org and GridDyn
Presenters:
Luigi Vanfretti (RPI) & Philip Top (LNLL)
luigi.vanfretti@gmail.com, top1@llnl.gov
Abstract:
The Modelica language, being standardized and equation-based, has proven valuable for the for model exchange, simulation and even for model validation applications in actual power systems. These important features have been now recognized by the European Network of Transmission System Operators, which have adopted the Modelica language for dynamic model exchange in the Common Grid Model Exchange Standard (v2.5, Annex F).
Following previous FP7 project results, within the ITEA 3 openCPS project, the presenters have continued the efforts of using the Modelica language for power system modeling and simulation, by developing and maintaining the OpenIPSL library: https://github.com/SmarTS-Lab/OpenIPSL
This seminar first gives an overview of the origins of the OpenIPSL and it’s models, it contrasts it against typical power system tools, and gives an introduction the OpenIPSL library. The new project features that help in the OpenIPSL maintenance (use of continuous integration, regression testing, documentation, etc.) are also described.
Finally, the seminar will present current work at LNLL that exploits OpenIPSL in coordination with other tools including ongoing work integrating openIPSL models into GridDyn an open-source power system simulation tool, as well as a demos of the use of openIPSL libraries in GridDyn.
Bios:
Luigi Vanfretti (SMIEEE’14) obtained the M.Sc. and Ph.D. degrees in electric power engineering at Rensselaer Polytechnic Institute, Troy, NY, USA, in 2007 and 2009, respectively.
He was with KTH Royal Institute of Technology, Stockholm, Sweden, as Assistant 2010-2013), and Associate Professor (Tenured) and Docent (2013-2017/August); where he lead the SmarTS Lab and research group. He also worked at Statnett SF, the Norwegian electric power transmission system operator, as consultant (2011 - 2012), and Special Advisor in R&D (2013 - 2016).
He joined Rensselaer Polytechnic Institute in August 2017, to continue to develop his research at ALSETLab: http://alsetlab.com
His research interests are in the area of synchrophasor technology applications; and cyber-physical power system modeling, simulation, stability and control.
Philp Top (Lawrence Livermore National Lab)
PhD 2007 Purdue University. Currently a Research Engineer at Lawrence Livermore National Laboratory in Livermore, CA. Philip has been involved in several projects connected with the DOE effort on Grid Modernization including projects on modeling and simulation, co-simulation and smart grid data analytics. He is the principle developer on the open source power system simulation tool GridDyn, and a key contributor to the HELICS open source co-simulation framework.
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AI Techniques for Smart Grids
1. AI Techniques for Smart Grids
Networked and Embedded Systems
Wilfried Elmenreich | 2014-05-22
Keynote lecture, ISGT-ASIA 2014
2. Introduction
• Many AI techniques are already in use
– Artificial neural networks (Modeling)
– Fuzzy logic (Control)
– Evolutionary algorithms,
– Swarm algorithms (Optimization)
• Now we go for the real thing
– should we change the way the system is
controlled?
Must?
4. What is a Self-Organizing System
„A self-organizing system (SOS) is a set of
entities that obtains global system behavior via
local interactions without centralized control.“
7. Characteristics
• System of many interconnected parts
• Degree of difficulty in predicting the system behavior
• Emergent properties
• Dynamic
• Decentralized control
• Global behavior from local interactions
• Robustness, adaptivity
• Non-linearity (small causes might have large effects)
7
Wilfried Elmenreich
8. SOS and Smart Grid
• Why a self-organizing approach?
8
Wilfried Elmenreich
Why Self-
Organzation?
Image: Creative Commons, Wikipedia
Figure: Creative Commons, Wikipedia
9. Transferring control to the network
• Counter-arguments
– Giving up control makes the system instable,
– untrustable,
– harder to maintain…
• Pro arguments
– Stability for complex system can
be only achieved by control
approach at same complexitiy level
– Self-organizing systems are more robust…
– and provide inherent scalability
• Sometimes you do not have this choice!
9
Wilfried Elmenreich
Image: Creative Commons, Wikipedia
10. Example: Wide Area Synchronous Grids
(Interconnections)
10
Wilfried Elmenreich
Figure: Creative Commons, transmission data based on European Joint
Research Center/Institute for Energy and Transport
• Operate at synchronized
frequency
• UCTE grid (Continental Europe)
is largest synchronous grid in
the world in terms of
generation capacity (667 GW)
• Unbundling process of
power generation and
Transmission System Operators
(TSO) many players
11. Oscillations in wide area grids
On Saturday, 19 February 2011 around 8:00 in the morning, inter-area
oscillations within the Continental Europe power system occurred. The
highest impact of these 0.25 Hz oscillations was observed in the
middle-south part of the system with amplitudes of +/- 100 mHz in
southern Italy and related power oscillation on several north-south
corridor lines of up to +/- 150 MW and with resulting voltage
oscillation on the 400 kV system of +/- 5 kV respectively.
ENTSO-E, ANALYSIS OF CE INTER-AREA OSCILLATIONS OF 19 AND 24 FEBRUARY 2011, 2011
Almost the same event reappeared on 24 February 2011 during
midnight hours
11
Wilfried Elmenreich
12. System frequency oscillations
12
Wilfried Elmenreich
• Superposition of 0.18 Hz (East-West Mode) and 0.25 Hz
(North-South Mode) modes
• Frequency and damping continously oscillates
Figure: ENTSO-E, ANALYSIS OF CE INTER-AREA OSCILLATIONS OF 19 AND 24 FEBRUARY 2011, 2011
13. Investigation of the oscillation events
• Transmission system operators (TSOs) Amprion, Mavir, TenneT
DE, Swissgrid,... exchanged power recordings
• Event was not predictable, no single cause
• Oscillations started around the change of the hour
– Turkey had changed mode displacement
• Total system load was low
• Absence of industrial load
• Dispersed generation (PV, Wind) provides less stabilized
inertia than classical generators
• Italian system currently more sensitive to oscillation modes
– Power system stabilisers in Italy had been reinforced
13
Wilfried Elmenreich
14. Observations from this example
• Liberalization of power market has decreased the scope of
control
• New approach is to carefully and knowledgeable interact with
the system in order to guide it
• We can can observe the main properties of a SOS here
• Understanding this system in a new way became a necessity
14
Wilfried Elmenreich
16. Smart Meter Rollout
• Energy Services Directive
(2006/32/EC) and the electricity
directive (2009/72/EC) require
the implementation of
"intelligent metering systems".
• Such systems ought to be in
place for 80% of electricity
consumers by end 2020
16
Wilfried Elmenreich
Source: The Smart Grid in Europe, 2012-2016: Technologies, Market
Forecasts and Utility Profiles (GTM Research), August 2011
17. The Smart Grid, as the Providers Envision it
• Smart meters
– Read meters remotely (save money for data acquisition)
– Get metering data at a high resolution
• Controllability of the loads
– Send „off“ signals to customer appliances at peak load situations
– Cut off a customer that does not pay the bill
• Having a system supporting different types of energy sources
and storage
in overall: get more comprehensive information and control
over the system
17
Wilfried Elmenreich
18. The Smart Grid, as the Customers want it
• Magically save energy / reduce bill
• Connect own generators (plug-in PV system)
• Get more reliable energy service
• Get green energy
• Don‘t give up privacy or control
in overall: only positive things should arise,
nothing must get worse
18
Wilfried Elmenreich
19. How Self-Organization can help
• Handling complexity: Provides scalable approaches for a high
number of interacting components
providers will like that
• „Bossless structure“: Allow bottom-up processes, keep
responsibility and decisions at customer („I can decide“)
customers will like that
Building Self-Organizing Systems 19
Wilfried Elmenreich
22. Rules of an SOS may be simple…
• ..but finding the right rules is difficult!
• Complex systems are hard
to predict
• Counter-intuitive
dependencies
22
Image: USGOV-NOAA (Public Domain)
Wilfried Elmenreich – Building Self-Organizing Systems
23. Evolutionary Design Approach
Building Self-Organizing Systems 23
Wilfried Elmenreich
• Evolution applied during design phase
• We don‘t refer to evolution/development of a system
at run time
24. Search Algorithm
Building Self-Organizing Systems 24
Wilfried Elmenreich
• Figuratively and literally a zoo on metaheuristic
optimization algorithms
• Ability to find global optimum
• Number of tweaking parameters?
25. FREVO: A Software for Designing SOS
• FREVO (Framework for Evolutionary Design)
• Operates on a simulation of the problem
• Interface for sensor/actuator connections to the agents
• Feedback from a simulation run -> fitness value
• Open-source, system-independent http://frevo.sourceforge.net
26. System architecture
Building Self-Organizing Systems 26
Wilfried Elmenreich
6 major components:
task description, simulation setup, interaction
interface, evolvable decision unit, objective function,
search algorithm
28. Application example: Trader (1)
• Evolving an energy trader algorithm at consumer/prosumer
level
• Simulation
• Java module added to FREVO
• Market rules
• Simulated Market
• Agent
• No initial knowledge
about market rules
• Trader rules are learned implicitly
• This way also counter-intuitive strategies are considered
29. Application example: Trader (2)
• Tradeoff between performance, complexity and
comprehensibility
There is no free lunch!
Performance of
evolved market agents
30. WiP: Evolving system of device-level traders
• Model HEMS devices as agents with independent controllers
• Constraints are given by a budget per device and the importance of a device
for the user
31. Summary
• AI techniques can be used as a tool but as well
contribute to a change in system design
• Self-organizing systems are promising for handling
complex systems
• Design challenge
– Evolutionary approach in combination with modelling
techniques
• Validation challenge
– Verification techniques, simulation
– Need for more case studies
31
Wilfried Elmenreich
32. Thank you very much for your attention!
Building Self-Organizing Systems 32
Wilfried Elmenreich
Thank you very much
for your attention!
Image: Creative Commons, Wikipedia