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What Is Artificial General Intelligence?



Artificial General Intelligence (AGI) is defined as the software representation of generalized human cognitive capacities that enables the AGI system to solve problems when presented with new tasks. 

In other words, it's AI's capacity to learn similarly to humans.



Strong AI, full AI, and general intelligent action are some names for it. 

The phrase "strong AI," however, is only used in few academic publications to refer to computer systems that are sentient or aware. 

These definitions may change since specialists from many disciplines see human intelligence from various angles. 

For instance, computer scientists often characterize human intelligence as the capacity to accomplish objectives. 

On the other hand, general intelligence is defined by psychologists in terms of survival or adaptation.

Weak or narrow AI, in contrast to strong AI, is made up of programs created to address a single issue and lacks awareness since it is not meant to have broad cognitive capacities. 

Autonomous cars and IBM's Watson supercomputer are two examples. 

Nevertheless, AGI is defined in computer science as an intelligent system having full or comprehensive knowledge as well as cognitive computing skills.



As of right now, there are no real AGI systems; they are still the stuff of science fiction. 

The long-term objective of these systems is to perform as well as humans do. 

However, due to AGI's superior capacity to acquire and analyze massive amounts of data at a far faster rate than the human mind, it may be possible for AGI to be more intelligent than humans.



Artificial intelligence (AI) is now capable of carrying out a wide range of functions, including providing tailored suggestions based on prior web searches. 

Additionally, it can recognize various items for autonomous cars to avoid, recognize malignant cells during medical inspections, and serve as the brain of home automation. 

Additionally, it may be utilized to find possibly habitable planets, act as intelligent assistants, be in charge of security, and more.



Naturally, AGI seems to far beyond such capacities, and some scientists are concerned this may result in a dystopian future

Elon Musk said that sentient AI would be more hazardous than nuclear war, while Stephen Hawking advised against its creation because it would see humanity as a possible threat and act accordingly.


Despite concerns, most scientists agree that genuine AGI is decades or perhaps centuries away from being developed and must first meet a number of requirements (which are always changing) in order to be achieved. 

These include the capacity for logic, tact, puzzle-solving, and making decisions in the face of ambiguity. 



Additionally, it must be able to plan, learn, and communicate in natural language, as well as represent information, including common sense. 

AGI must also have the capacity to detect (hear, see, etc.) and output the ability to act, such as moving items and switching places to explore. 



How far along are we in the process of developing artificial general intelligence, and who is involved?

In accordance with a 2020 study from the Global Catastrophic Risk Institute (GCRI), academic institutions, businesses, and different governmental agencies are presently working on 72 recognized AGI R&D projects. 



According to the poll, projects nowadays are often smaller, more geographically diversified, less open-source, more focused on humanitarian aims than academic ones, and more centered in private firms than projects in 2017. 

The comparison also reveals a decline in projects with academic affiliations, an increase in projects sponsored by corporations, a rise in projects with a humanitarian emphasis, a decline in programs with ties to the military, and a decline in US-based initiatives.


In AGI R&D, particularly military initiatives that are solely focused on fundamental research, governments and organizations have very little roles to play. 

However, recent programs seem to be more varied and are classified using three criteria, including business projects that are engaged in AGI safety and have humanistic end objectives. 

Additionally, it covers tiny private enterprises with a variety of objectives including academic programs that do not concern themselves with AGI safety but rather the progress of knowledge.

One of the most well-known organizations working on AGI is Carnegie Mellon University, which has a project called ACT-R that aims to create a generic cognitive architecture based on the basic cognitive and perceptual functions that support the human mind. 

The project may be thought of as a method of describing how the brain is structured such that different processing modules can result in cognition.


Another pioneering organization testing the limits of AGI is Microsoft Research AI, which has carried out a number of research initiatives, including developing a data set to counter prejudice for machine-learning models. 

The business is also investigating ways to advance moral AI, create a responsible AI standard, and create AI strategies and evaluations to create a framework that emphasizes the advancement of mankind.


The person behind the well-known video game franchises Commander Keen and Doom has launched yet another intriguing endeavor. 

Keen Technologies, John Carmack's most recent business, is an AGI development company that has already raised $20 million in funding from former GitHub CEO Nat Friedman and Cue founder Daniel Gross. 

Carmack is one of the AGI optimists who believes that it would ultimately help mankind and result in the development of an AI mind that acts like a human, which might be used as a universal remote worker.


So what does AGI's future hold? 

The majority of specialists are doubtful that AGI will ever be developed, and others believe that the urge to even develop artificial intelligence comparable to humans will eventually go away. 

Others are working to develop it so that everyone will benefit.

Nevertheless, the creation of AGI is still in the planning stages, and in the next decades, little progress is anticipated. 

Nevertheless, throughout history, scientists have debated whether developing technologies with the potential to change people's lives will benefit society as a whole or endanger it. 

This proposal was considered before to the invention of the vehicle, during the development of AC electricity, and when the atomic bomb was still only a theory.


~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram


You may also want to read more about Artificial Intelligence here.

Be sure to refer to the complete & active AI Terms Glossary here.


Cyber-Physical System Approach To Smart Grids



    What Are Smart Grids?

    Smart grids are electric grids that use sophisticated monitoring, control, and communication technology to deliver dependable and secure electricity, increase system efficiency, and give flexibility to prosumers. 

    The advent of the smart grid era, as well as advancements in contemporary infrastructures for metering, communication, and energy storage, has transformed the power grid. 

    Smart grids are created using complicated physical networks and cyber technologies, allowing smart grids to be used in the Internet of Energy (IoE)



    IoE is the cloud in which intelligent sources of power production and loads of power consumption are incorporated. 

    Sensors are installed into modern electric power systems to give measurements. 

    Sensor measurements, as well as the sophisticated uses of numerous sensors, need the employment of a Cyber-Physical System (CPS)

    CPS is a system type that combines physical processes, computing, and networking. 

    The smart grid CPS model aids in modelling and simulation for evaluating system performance and characteristics. 

    The smart grid's CPS model must be resilient, allow for future additions, and be compatible with web service technologies. 

    The physical layers of the smart grid include electricity generating sources and loads such as smart buildings, the cyber-physical integration is formed by sensors used for measurements, and the cyber layer is formed by data storage and processing utilizing IOE. 

    The smart grid CPS model aids in the integration of intelligent devices and related information and communication technologies for resilient and dependable smart grid operation. 

    The energy management system is critical in the smart grid paradigm for increasing system efficiency and reliability. 

    This article describes a CPS model for smart grids as well as the obstacles related with its development. 

    Furthermore, this article discusses the smart grid's energy management system paradigm.





    What Is The History Of Smart Grids?


    The complex interactive network was the first study carried out by the Electric Power Research Institute (EPRI) in 1998 for constructing a fully automated dependable grid, which is the first smart grid prototype. 

    Following Intelli-suggestion Grid's in 2002, the smart grid idea was largely adopted for the future development of electricity networks. 

    In 2005, the European Smart-Grids Technology Platform was established, and in 2006, a study containing ideas and a framework for the European smart grid was released. 

    The US Department of Energy revised this research in 2007 to promote a stable and sustainable energy supply in the report "The Smart Grid." Many nations' key task is to establish a smart city with socioeconomic and environmental advantages, as well as to improve power consumption in a smarter manner to preserve energy. 

    In recent years, there has been a rise in demand for electric energy, with load patterns growing more complicated, posing a challenge to the power grid network. 

    To overcome these issues, power engineers and academics introduced the CPS strategy for the power system network. 



    The Cyber-Physical Energy Systems (CPES) are a mix of a cyber-physical system and a power system network. 

    The measurements from the sensors are used to make the choice to execute the control in the distributed network. 

    The integration of cyber and physical components in the system is the most difficult problem in CPES. 

    For a unified functioning of cyber and physical layer components in the CPES, all events occurring and choices made must be communicated between cyber and physical layer components, boosting the system's potential to resolve difficulties. 

    The development of smart sensors and their integration into the electric grid guarantees that original and reliable data is available at control centers. 

    The use of genuine, trustworthy data improves the precision with which issues are solved and control is applied in a variety of applications. 

    The current electric grid incorporates a greater quantity of renewable energy, which is not properly managed owing to the intermittent availability of renewable energy. 

    This difficulty inspires the notion of a smart grid with upgraded infrastructure for communication and computing in the traditional grid. 

    The current smart grid apps lack an underlying framework, resulting in isolation and making integration and future growth difficult. 

    The smart grid may be constructed more effectively with the aid of a CPES reference model. 

    The CPES reference model must handle large-scale and long-term smart grid scenarios. 

    The CPES model must be a general model that represents the features of the smart grid scenario using smart grid technology and standards. 

    The primary goal of power engineers is to create an efficient algorithm that can operate in real time in the grid. 

    Another challenge for power engineers is the extraordinary amount of data collected by the Phasor Measurement Unit (PMU), which must be gathered and processed according to the requirements. 

    The coordination of dispersed resources is critical to the grid's real-time functioning. 

    The grid's communication network must be upgraded in order to manage the grid's coordinated functioning. 

    The infrastructure consists of a communication network and middleware, which includes software for data processing and control deployment. 

    The diagram below depicts the structure of a typical electricity network. 



    This article provides an overview of CPS as well as a summary of research in cyber-physical energy systems. 

    The purpose of this article is to assist power engineers and researchers in understanding the CPS approach to power grids. 



    What are the various definitions of a  SMART GRID across geographies?


    Smart grid (SG) is a phrase that has many meanings. 

    The definition of SG is the integration and enablement of information and communications technology (ICT) and advanced technologies with power networks in order to make the power system efficient, affordable, and sustainable. 

    In the United States, SG refers to the process of converting the electric industry from a centralized, producer-controlled network to a consumer-controlled network. 

    SG in Europe refers to the involvement and integration of all European societies

    SG refers to a physical network-based method to ensuring security, dependability, and sustainability in China. 

    The need of SG in IEEE Grid Vision 2050 is to run and manage the whole power system, which includes all current and future technologies. 

    The need for flexible, portable, safe, and secure power supply utilization through SG necessitates a rethinking of the interplay between physical power systems, cyber systems, and users. 

    The issues with SGs include the intermittent nature of renewable generating, which impacts power quality and system stability. 

    Peak demand plays an important role in electricity consumption; lowering peak demand improves supply capacity without adding additional generating units. 

    Power losses in SGs may be reduced by eliminating long-distance transmission lines owing to the use of dispersed generating. 

    The use of smart meters, modern sensors, and ICT aids in the improvement of SG efficiency. 


    What are the characteristics of a smart grid?


    To accomplish all of the aforementioned benefits, the SGs must have the following characteristics: 


    • Distributed control 

    • Load forecasting 

    • Renewable generation forecasting 

    • Peak demand reduction

     • Energy storage system 

    The CPS paradigm, which employs a systematic approach to solving difficulties and challenges, is the answer to these concerns and challenges. 


    What is a  CYBER-PHYSICAL SYSTEM?


    The term CPS was established in 2006 by the National Science Foundation to characterize a complex, interdisciplinary, next-generation system that integrates embedded technology in the physical environment. 

    CPS is defined in the United States as the integration of embedded systems and physical components, in Europe as the communication between cloud and human, and in China as intelligence in sensing, processing, and control. 

    CPS has made considerable progress in recent years, but it still has a long way to go before reaching its full potential. 

    Sensing, analyzing, synthesizing, modelling, and control are rapidly evolving in engineering and research. 

    CPSs combine engineering and computer science to address concerns and challenges. 


    The following are the technical obstacles in bringing the two sectors together: 


    • Design: 

    1. Design is essential for achieving continuous integration, communication, and computation. 
    2. To satisfy the system requirements, standard architectures and design tools are necessary. 
    3. Architectures and approaches should provide data confidentiality, integrity, availability, and asset protection. 

    • Science and engineering: 

    1. The integration of cyber and physical components necessitates rapid sensing, processing, and control, all of which must be precise. 
    2. To make judgments and govern activities, enormous amounts of data must be processed quickly and efficiently. 
    3. Traditional centralized control does not have the requisite speed, hence dispersed control is essential. 
    4. The essential components are data sensing, data processing, and control. 


    What is a GENERALIZED CPS MODEL? 


    The physical layer comprises of the components required for CPS monitoring and control. 

    Equipment in the physical layer include generators, transformers, loads, and measurement devices. 


    The multi-input multi-output (MIMO) CPS model is represented as, 

    ẋ(t) = Ax(t) + Bu(t) (1.1) 

    y(t) = Cx(t) (1.2) 

    where, A is the state matrix, B is the input matrix, C is the output matrix, 

    x(t) is the state vector, u(t) is the input vector, y(t) is the output vector. 

    The control is achieved using input vector which is given by, 

    u(t) = Kx(t) (1.3) 

    where, K is the connection between cyber layer control and physical layer sensors. 



    What Is The CPS APPROACH TO SMART GRID?


    To accomplish CPS features, SG integrates physical components of the power grid network with the cyber layer. 

    Real and virtual systems are combined in SG, where events in physical systems are relayed as input to CPS control centers and simulated to assess physical system performance. 

    Communication channels enable dynamic collaboration between physical and cyber systems. 

    Parallel processing and distributed data aid in decision making via CPS layers. 


    The CPS will adapt, organize, and learn on its own, allowing it to react to faults, attacks, and emergencies in SG, making it secure and trustworthy. 

    The problems in the CPS part of the SG are that the system is time-critical, the components work together to ensure stability, voltage and frequency control, and quick reaction to uncertainties and disturbances. 

    CPS is used in SG to eliminate duplication and increase SG stability. 


    The primary functions of CPS in Singapore are as follows: 

    • Dependability 

    • Reliability 

    • Predictability 

    • Sustainability 

    • Security 

    • Interoperability 

    Many studies are being conducted to address the concerns related to SG and CPS. 

    The combination of SG and CPS is referred to as CPES. 


    What is the Architecture of a CPS Smart Grid?

    The physical component of the power system network, which varies from other object-oriented software, demands safety and dependability. 

    To enable the system to function in uncertain and unexpected settings, the CPS design must be particular for integrating cyber and physical components. 

    In these conditions, the software-based components perform well. 

    Because software platforms lack timing capabilities, redesigning computer architectures in light of power system features is essential. 

    It requires standards and frameworks in which physical, communication, and computing components interface on their own standards and are interfaced together. 

    Communication technology is required for efficient and effective interaction between physical and cyber layer components. 

    Space and time are two dimensions of communication that must be addressed. 

    They relate to the distance and time required for data transmission. 

    The several levels of communication are determined by the network size, and they are as follows: home area network, neighborhood area network, metropolitan area network, and wide area network. 

    Time delay, error pockets, and queue delays are all important real-time issues. 

    Modeling and Simulation: The modelling tool must be able to handle network standards, interoperability, hybrid modelling, and large-scale operation. 

    The SG of the future might be huge or small in size, with scattered sources and energy needs, and it must be run reliably and in a user-friendly way, requiring risk analysis, risk management, security, uncertainty analysis, and coordination. 


    How Is Cyber Security Enforced In a CPS Smart Grid?

    CPS must be secure, and any random failure or attack would be detrimental to the system. 

    Because of the employment of cyber components such as PMU and advanced metering infrastructure (AMI), the system is vulnerable to attack. 


    The SG must be designed to identify and mitigate cyber-attacks using an intrusion detection system (IDS). 

    IDS is classified into two types: 

    1. host-based IDS 
    2. and network-based IDS. 


    How does Distributed Computation occur on a CPS Smart Grid?

    In SG, a vast number of smart meters and sensors are positioned at different levels, each of which must process massive amounts of data sequentially. 

    Fault detection, power control and reconfiguration, management, and restoration in power networks are all time-sensitive, posing a problem in SG. 

    Data mining techniques ideal for dealing with massive amounts of data are the answer to these issues. 

    In SG, new computational techniques like as grid and cloud computing platforms are employed to execute sub- and local calculations. 

    Distributed Intelligence: The SGs use a multi-agent system to do large-scale computing (MAS). 

    An agent is a control entity that may communicate and interact with other components to achieve local/global objectives. 

    In MAS, a set of agents is used in a dispersed network to concentrate on different applications. 

    In SG, automation must be present at both the micro and macro operational levels in order to make requirements-based decisions. 

    SG is dependent on global optimization and local control, where global optimization has multiple goals and local control has one. 

    Because centralized optimization is ineffective for SG, distributed optimization solutions are necessary. 

    In the merging of global optimization with local control, MAS is employed to achieve global coordination. 


    How is Distributed Control achieved in a Smart Grid?

    As the number of components in SG increases, the system gets more complicated due to the presence of several levels of control and hierarchy. 

    Control goals are multi-objective, having global and local needs that may vary based on the operating conditions. 

    The control must generate data from physical components in order to assess and regulate the system's components. 

    Control in SG is built on the physical layer, the cyber layer, and the planning and operations layer. 

     


    What Is CPES?



    Power system analysis in contemporary systems is performed using computer models and is a current research area. 

    When computers were used for the power grid, new software to mimic the transmission and distribution system was created. 

    By building additional software, this system was upgraded to compute more complicated networks and to calculate quicker. 

    The study in the creation of new software aided in the construction of a distributed model of a power network, parallel processing, and system analysis. 

    Some current power system simulators, such as Siemens PSSE, are only used for transmission systems, while others, such as GridLab-D, are only used for distribution systems. 

    Recently, considerable research has been conducted in the field of co-simulation. 

    Many cloud-based software applications are utilized to simulate the network and run simulations. 

    Co-simulation combines continuous system and discrete event simulation to model and understand CPES behavior. 

    Diverse strategies, such as the common information model, have been used to integrate various systems. 

    These approaches are used to manage energy in distributed systems and subsystems. 

    Many researchers have used the CPS for power grid design to examine the reliability and security of the power system. 

    The study focuses on the difficulties in CPS modelling, design, and simulation. 

    CPS is utilized in a variety of applications, including management, smart buildings, cloud computing, surveillance, scheduling, monitoring, and transportation systems. 

    16 Any outage or blackout in the power system has a significant influence on the economy and society, making the functioning of the power grid important. 

    The CPES model is an interconnected structure meant to facilitate communication among stakeholders. 

    This model is not a CPES standard, but it contains information on numerous technologies and standards for the smart grid, such as the National Institute of Standards and Technology (NIST) and IEC. 

    This model may be used to create new technologies, standards, or algorithms, as well as to assess the operation of smart grids. 

    Future standards and technologies may be grown from current ones using the CPES paradigm. 


    The following reference models have been established and discussed in the literature: 


    • Open Systems Interconnection Reference Model. 

    • Agent Systems Reference Model. 

    • National Institute of Standards and Technology Reference Model. 



    WHAT ARE THE REQUIREMENTS AND CHALLENGES IN SMART GRID MODELING?


    Many issues in monitoring and controlling power networks have arisen in recent years as a result of technological advancements and the usage of dispersed energy sources. 

    Many PMUs have been installed to collect real-time data and transmit it to the control center. 

    PMUs collect data at a high sample rate. 

    To accomplish this high sample rate, a Wide Area Network (WAN) is employed to develop Wide Area Monitoring and Control (WAMC). 

    WAMC has several applications in power grids, including state estimation, contingency analysis, optimum power flow analysis, economic dispatch, and autonomous generation control. 

    These acquired data are used to operate systems using control algorithms, however all data must be monitored synchronously to prevent mistakes, which are done in the underlying framework. 

    As a result, the underlying infrastructure is a critical component for power system applications. 

    Applications may be both functional and nonfunctional. 

    The functional applications synchronize and coordinate data flow across dispersed network resources. 

    Scalability (support for a large number of PMUs and a communication network), latency and predictability (time sensitive), and reconfigurability are nonfunctional applications (addition or removal of components, nodes, or modifications in control algorithms). 


    WHAT IS THE  SMART GRID ENERGY MANAGEMENT SYSTEM (SGEMS)?


    The AMI devices in the SG paradigm are used for two-way communication between the utility and the user, allowing for demand control by shifting peak loads. 

    It is an optimum management system for monitoring and managing power production, consumption, and storage in SGs. 

    The network's communication infrastructure is utilized to gather data on load demand, generation, and forecasts from all sensors in order to enable remote monitoring and control for different operating modes, which is monitored in control centers. 

    The SGEMS not only delivers efficient generation use, but also energy storage and system management services. 



    What is the SGEMS ARCHITECTURE?


    The SGEMS center features a central controller that provides monitoring and control capabilities to the utility and customer based on communication. 

    The smart meter serves as a conduit for communication between the utility and the customer. 

    The data is collected by smart meters and sent to the control center, which receives the control signal to optimize demand management depending on generation. 

    Electric vehicles (EV) use SG electricity while simultaneously providing power back to SG in an emergency and acting as energy storage. 

    Because the distributed generation in the SG is integrated to accomplish generation management, the SG does not need to depend on electricity from the central grid. 

    Because renewable energy is inherently intermittent, energy storage systems play a critical role in maintaining power quality, efficiency, and dependability. 



    What are the FUNCTIONS OF SGEMS?


     The SGEMS must be adaptable in order to manage and regulate the SG and participate in the market while conserving energy and meeting load demand. 

    Control services are accessible to utilities and customers, who may choose services and preferences through a human-machine interface. 


    The following are the primary roles and descriptions of the SGEMS: 


    Monitoring: Provides access to data on energy production and demand Displays operating mode and status 

    Logging: Collect and preserve data on DER production, demand from loads, and energy storage system. 

    Control: There are two forms of control: direct control applied on equipment and remote control where consumers watch load patterns and control. 

    Alarm: An alarm is raised at the SGEMS centre with data on system anomalies discovered. 

    Management: Management improves the optimization and effective use of energy in Singapore. 

    It offers services including DER management and storage management. 


    What is the INFRASTRUCTURES OF SGEMS?

    A smart control center, smart meter, communication and networking system, energy storage, distributed generation, and other smart devices comprise the SGEMS infrastructure. 

    SGEMS can access, monitor, regulate, and optimize the performance of multiple distributed generations, loads, and other devices via these infrastructures. 

    SGEMS facilitates load and generation integration via two-way communication. 



    COMMUNICATIONS NETWORK:

    The SGEMS are based on hardware communication, such as powerline communication and human-machine interface. 

    Researchers are working on new WAN communication networks. 

    The SGEMS communication network must fulfil the IEEE 802.15.4 WAN specifications. 

    The ability to include Bluetooth technology in communication may be employed in SGEMS as well. 

    To accomplish system functioning, the major components of SGEMS are the processor for applications, communication, user, sensor, and load interface. 


    THE SMART METER:

    Smart meters are used to assess consumer energy use and production, as well as power generation, and they employ two-way communication to send data to and receive signals from the control center. 

    Smart meters' primary features include detecting energy use, two-way communication, transmitting data and receiving instructions, smart load shedding transition in the event of a failure, and data collecting. 


    CENTER FOR SMART ENERGY MANAGEMENT SYSTEMS (EMS). 

    The Smart Energy Management System (EMS) center is the brain of the whole smart grid and is responsible for implementing the energy management system in SG. 

    The smart EMS center's primary functions are as follows: receiving messages sent by smart meters and control panels, automated demand response, human-machine interface, online monitoring, scalability, integrating distributed resources and energy storage, forecasting renewable generation, and optimal control. 


    What is the scope for Renewable Sources in IN SMART GRIDS?


    Since the 1990s, the use of renewable energy has increased significantly in a variety of industrial, commercial, and residential settings. 

    Only 31.1% of all energy output in the globe is produced by renewable energy. 

    The research in the area of EMS for a renewable energy system is progressing rapidly. 

    The necessity to reduce emissions in energy generation paves the way for the development of sustainable approaches that make use of renewable energy sources. 



    How is the USE OF RENEWABLE ENERGY SOURCES IN EMS?

    Solar energy is the most environmentally friendly and inexhaustible renewable energy source. 

    Solar energy is used in a variety of ways, including solar heaters, solar PV, and so on. 

    Because of their ease of installation, solar heaters are often employed in home applications. 

    Solar PV and solar concentrators are used to generate electricity. 

    Electricity generating need large-scale investment to meet bulk power requirements. 

    Solar energy is used in two ways: 

    1. solar thermal, which converts sunlight into thermal energy and generates electricity, 
    2. and solar PV, which generates electricity directly from sunlight. 

    Solar energy is widely employed because of its copious supply of sunshine and minimal maintenance. 

    Because solar energy is only accessible during the day, energy storage is required. 

    Charge controllers are required to safeguard energy storage devices from overcharging and discharging. 

    Wind energy is another renewable energy source that is used on a big and small scale. 

    Wind speeds of 2-15 m/s can be used to generate electricity. 



    What are FUTURE DIFFICULTIES AND SCOPE OF SMART GRIDS? 


     In this part, important difficulties and possibilities in the SG's CPS are discussed in terms of ecosystems, big data, cloud computing, and the Internet of Things. 


    • Ecosystem Perspective: 

    • SG growth is inextricably linked to the environment and social system. 
    • The flora and fauna, as well as climate changes, are examples of nature, environment, and ecosystems that are influenced by SG improvement. 


    • Big Data: 

    • Big data is utilized in data collection and analysis. 
    • Volume, velocity, veracity, variance, and value are the five key features of big data. 


    • Cloud Computing: 

    • In SG, dispersed resources in real-time management must be met on time. 
    • Cloud computing is a model in which services like compute, networking, and storage serve as resources. 
    • It provides the benefits of self-service, resource sharing, flexibility, and it boosts security and overcomes privacy problems. 

    • Internet of Things (IoT): 

    • The Internet of Things (IoT) is the expansion of Internet services caused by the spread of RFID, sensors, smart devices, and "things" on the Internet. 
    • IoT is rapidly expanding, with 50 billion devices expected to be connected to the internet by 2020. 
    • The progress of IoT results in the advancement of IoE. 



     FINAL REMARKS AND THOUGHTS ON THE FUTURE OF  SMART GRIDS.


     The smart grid environment with EMS plays an important role in the efficient use of power and demand response. 

    The smart SGEMS with wireless networks and smart sensing element technology raises the bar for SG standards. 

    Because of its ease of use and simplicity, SGEMS has been more popular in recent years. 

    The present smart grid infrastructure, which includes two-way communication, metering, and monitoring devices, lays the groundwork for smart SGEMS applications. 

    The extensive use of SGEMS in the future may change the manner of electricity use and renewable energy utilization inside the power network. 

    Due to geographical and climatic conditions, alternative energy may be the dominant contributor in renewable energy applications, whereas wind and biomass contribute comparably little. 

    The use of renewable energy reveals that energy savings from transmission energy losses and conventional installation may be realized. 

    The design of a CPS is more difficult than developing physical and cyber components one by one. 

    For CPSs, the required behavior of machine parts must be specified in terms of their impact on the physical environment. 

    As a result, modelling requires a unifying framework that allows for consistency and a low-overhead style.


    ~ Jai Krishna Ponnappan.

    Find Jai on Twitter | LinkedIn | Instagram


    You may also read more about Green Technologies and Renewable Energy Systems here.



    References And Further Reading:


    1. Amin, M. Minimizing Failure While Maintaining Efficiency of Complex Inter-active Networks and Systems: EPRI and US Department of Defense Complex Interactive Networks/Systems Initiative; First Annual Report, 2000. 

    2. Haase, P. Intelli Grid: A Smart Network of Power. EPRI J. 2005, 27, 17–25. 

    3. Profiling and Mapping of Intelligent Grid R & D Programs. EPRI 2006.

    4. European Smart-grids Technology Platform: Vision and Strategy for Europe’s Electricity Networks of the Future. Directorate-General for Research Sustainable Energy Systems, 2006. 

    5. Davis, C. et al. Scada Cyber Security Testbed Development. In NAPS. IEEE, 2006; pp 483–488. 

    6. Schneider, K. et al. Assessment of Interactions Between Power and Telecommunications Infrastructures. IEEE TPWRS 2006. 

    7. Sun, Y. et al. Verifying Noninterference in a Cyber-physical System the Advanced Electric Power Grid. In QSIC; IEEE 2007; pp 363–369. 

    8. Karnouskos, S. Cyber-physical Systems in the Smartgrid. In INDIN. IEEE, 2011; pp 20–23. 

    9. Giani, A. et al. The Viking Project: An Initiative on Resilient Control of Power Networks. In ISRCS. IEEE, 2009; pp 31–35. 

    10. Mo, Y. et al. Cyber–physical Security of a Smart Grid Infrastructure. Proc. IEEE 2012, 100 (1), 195–209. 

    11. Susuki, Y. et al. A Hybrid System Approach to the Analysis and Design of Power Grid Dynamic Performance. Proc. IEEE 2012. 

    12. Saber, A.; Venayagamoorthy, G. Efficient Utilization of Renewable Energy Sources by Gridable Vehicles in Cyber-physical Energy Systems. Syst. J. IEEE 2010, 4 (3), 285–294. 

    13. Zhu, Q. et al. Robust and Resilient Control Design for Cyber-physical Systems with an Application to Power Systems. In CDC-ECC, 2011. 

    14. Hadjsaid, N. et al. Modeling Cyber and Physical Interdependencies-application in ICT and Power Grids. In IEEE/PES PSCE, 2009; pp 1–6. 

    15. Zhao, J. et al. Cyber Physical Power Systems: Architecture, Implementation Techniques and Challenges. Dianli Xitong Zidonghua (Autom. Electric Power Syst.) 2010, 34 (16), 1–7. 

    16. NIST Special Publication 1108R2. NIST Framework and Roadmap for Smart Grid Interoperability Standards, Release 2.0, 2012; http://www.nist.gov/smartgrid/upload/ NIST Framework Release 2-0 corr.pdf 

    17. Liu, Y.; Ning, P.; Reiter, M. False Data Injection Attacks against State Estimation in  Electric Power Grids. ACM TISSEC 2011. 


    What Is The SSLV Rocket?



      What Is SSLV?

      The Small Satellite Launch Vehicle (SSLV) is an ISRO-developed small-lift launch vehicle with a payload capacity of 500 kg (1,100 lb) to low Earth orbit (500 km (310 mi)) or 300 kg (660 lb) to Sun-synchronous orbit (500 km (310 mi)) for launching small satellites, as well as the ability to support multiple orbital drop-offs. 




      SSLV is designed with low cost and quick turnaround in mind, with launch-on-demand flexibility and minimum infrastructure needs. 

      The SSLV-D1 launched from the First Launch Pad on August 7, 2022, but failed to reach orbit. 

      SSLV launches to Sun-synchronous orbit will be handled in the future by the SSLV Launch Complex (SLC) at Kulasekharapatnam in Tamil Nadu




      After entering the operational phase, the vehicle's manufacture and launch operations would be handled by an Indian consortium led by NewSpace India Limited (NSIL). 


      What Is The Origin And Evolution Of SSLV?



      The SSLV was created with the goal of commercially launching small satellites at a far lower cost and with a greater launch rate than the Polar Satellite Launch Vehicle (PSLV)

      SSLV has a development cost of 169.07 crore (US$21 million) and a production cost of 30 crore (US$3.8 million) to 35 crore (US$4.4 million). 

      The expected high launch rate is based on mostly autonomous launch operations and simplified logistics in general. 

      In comparison, a PSLV launch employs 600 officials, but SSLV launch procedures are overseen by a tiny crew of about six persons. 



      The SSLV's launch preparation phase is predicted to be less than a week rather than months. 



      The launch vehicle may be erected vertically, similar to the current PSLV and Geosynchronous Satellite Launch Vehicle (GSLV), or horizontally, similar to the decommissioned Satellite Launch Vehicle (SLV) and Augmented Satellite Launch Vehicle (ASLV)


      The vehicle's initial three stages employ HTPB-based solid propellant, with a fourth terminal stage consisting of a Velocity-Trimming Module (VTM) with eight 50 N reaction control thrusters and eight 50 N axial thrusters for altering velocity. 


      SSLV's first and third stages (SS1) are novel, while the second stage (SS2) is derived from PSLV's third stage (HPS3). 



      Where Is The SSLV Launch Complex?



      Early developmental flights and those to inclined orbits would launch from Sriharikota, first from existing launch pads and ultimately from a new facility in Kulasekharapatnam known as the SSLV Launch Complex (SLC). 

      In October 2019, tenders for production, installation, assembly, inspection, testing, and Self Propelled Launching Unit (SPU) were announced. 

      When completed, this proposed spaceport at Kulasekharapatnam in Tamil Nadu would handle SSLV launches to Sun-synchronous orbit. 


      What Is The History Of The SSLV?

      Rajaram Nagappa recommended the development route of a 'Small Satellite Launch Vehicle-1' to launch strategic payloads in a National Institute of Advanced Studies paper in 2016. 



      S. Somanath, then-Director of Liquid Propulsion Systems Centre, acknowledged a need for identifying a cost-effective launch vehicle configuration with 500 kg payload capacity to LEO at the National Space Science Symposium in 2016, and development of such a launch vehicle was underway by November 2017. 



      The vehicle design was completed by the Vikram Sarabhai Space Centre (VSSC) in December 2018. 

      All booster segments for the SSLV first stage (SS1) static test (ST01) were received in December 2020 and assembled in the Second Vehicle Assembly Building (SVAB). 

      On March 18, 2021, the SS1 first-stage booster failed its first static fire test (ST01). 

      Oscillations were detected about 60 seconds into the test, and the nozzle of the SS1 stage disintegrated after 95 seconds. 

      The test was supposed to last 110 seconds. 

      SSLV's solid first stage SS1 must pass two consecutive nominal static fire tests in order to fly. 

      In August 2021, the SSLV Payload Fairing (SPLF) functional certification test was completed. 

      On 14 March 2022, the second static fire test of SSLV first stage SS1 was performed at SDSC-SHAR and satisfied the specified test goals. 


      How Will The Small Satellite Launch Vehicle (SSLV) Be Manufactured?

      ISRO has begun development of a Small Satellite Launch Vehicle to serve the burgeoning global small satellite launch service industry. 

      NSIL would be responsible for manufacturing SSLV via Indian industry partners. 

       

      What Are The Unique Features Of The Small Satellite Launch Vehicle (SSLV)?

      SSLV has been intended to suit "Launch on Demand" criteria while being cost-effective. 

      It is a three-stage all-solid vehicle capable of launching up to 500 kilograms satellites into 500 km LEO. 

      What Are The Expected Benefits Of The SSLV Rocket?

      Reduced Turn-around Time Launch on Demand Cost Optimization.

      Realization and Operation Ability to accommodate several satellites.

      Minimum infrastructure required for launch Design practices that have stood the test of time.

      The first flight from SDSC SHAR was originally scheduled during the fourth quarter of 2019. It occurred only in August of 2022.

      Following the first developmental flights, ISRO plans to produce SSLV via Indian Industries through its commercial arm, NSIL. 


      What Is The Operational Performance History Of The SSLV?


      The SSLV's maiden developmental flight was place on August 7, 2022. 

      SSLV-D1 was the name of the flying mission. 

      The SSLV-D1 flight's mission goals were not met. 

      The rocket featured three stages and a fourth Velocity Trimming Module (VTM). 

      The rocket stood 34m tall, with a diameter of 2m, and a lift-off mass of 120t in its D1 version. 

      The rocket launched EOS 02, a 135 kilograms Earth observation satellite, and AzaadiSAT, an 8 kg CubeSat payload designed by Indian students to promote inclusion in STEM education. 


      The SSLV-D1 was planned to deploy the two satellite payloads in a circular orbit with a height of 356.2 km and an inclination of 37.2°. 

      The ISRO's stated reason for the mission's failure was software failure. 

      The mission software identified an accelerometer anomaly during the second stage separation, according to the ISRO. 

      As a result, the rocket navigation switched from closed loop to open loop guidance. 

      Despite the fact that this change in guiding mode was part of the redundancy incorporated into the rocket's navigation, it was not enough to save the mission. 

      During open loop guiding mode, the last VTM stage only fired for 0.1s rather than the required 20s. 

      As a result, the two satellites and the rocket's VTM stage were injected into an unstable elliptical 35676 km orbit. 

      The SSLV-final D1's VTM stage had 16 hydrazine-fueled (MMH+MON3) thrusters. 

      Eight of them were to regulate the orbital velocity and the other eight were to control the altitude. 

      During the orbital insertion maneuvers, the VTM stage also controlled pitch, yaw, and roll. 

      The SSLV-three D1's major stages all worked well. 

      However, this was insufficient to provide enough thrust for the two satellite payloads to establish stable orbits. 

      The VTM stage required to burn for at least 20 seconds to impart enough extra orbital velocity and altitude adjustments to put the two satellite payloads into their designated stable orbits. 

      Instead, the VTM activated at 653.5s and shut down at 653.6s after lift-off. 

      After the VTM stage was partially fired, the EOS 02 was released at 738.5s and AazadiSAT at 788.4s after liftoff. 

      These failures occurred, causing the satellites to reach an unstable orbit and then be destroyed upon reentry. 



      What Was The Performance Outcome Of The SSLV D1 Mission?

      SSLV's maiden developmental flight. 

      The mission goal was a circular orbit of 356.2 km height and 37.2° inclination. 

      Two satellite payloads were carried on the trip. 


      1. The 135-kilogram EOS-02 Earth observation satellite 
      2. and the 8-kilogram AzaadiSAT CubeSat. 


      Due to sensor failure and flaws in onboard software, the stage and two satellite payloads were put into an unstable elliptical orbit of 35676 km and then destroyed upon reentry. 

      The mission software, according to the ISRO, failed to detect and rectify a sensor malfunction in the VTM stage. 

      The last VTM stage only fired momentarily (0.1s). 


      What Were The Overall Lessons From The SSLV-D1/EOS-02 Mission?



      Mission ISRO developed a small satellite launch vehicle (SSLV) to launch up to 500 kilograms satellites into Low Earth Orbits on a 'launch-on-demand' basis . 


      The SSLV-D1/EOS-02 Mission's first developmental flight was slated for August 7, 2022, at 09:18 a.m. 

      (IST) from the Satish Dhawan Space Centre's First Launch Pad in Sriharikota. 

      The SSLV-D1 mission would send EOS-02, a 135 kilograms satellite, into a low-Earth orbit 350 kilometers above the equator at an inclination of roughly 37 degrees. 

      The mission also transports the AzaadiSAT satellite. 

      SSLV is built with three solid stages weighing 87 t, 7.7 t, and 4.5 t. 

      The satellite is inserted into the desired orbit using a liquid propulsion-based velocity trimming module. 

      • SSLV is capable of launching Mini, Micro, or Nanosatellites (weighing between 10 and 500 kg) into a 500 km planar orbit. 
      • SSLV gives low-cost on-demand access to space. 
      • It has a quick turnaround time, the ability to accommodate numerous satellites, the ability to launch on demand, minimum launch infrastructure needs, and so on. 



      SSLV-D1 is a 34-meter-tall, 2-meter-diameter vehicle with a lift-off mass of 120 tonnes. 

      ISRO developed and built the EOS-02 earth observation satellite. 



      This microsat class satellite provides superior optical remote sensing with excellent spatial resolution in the infrared spectrum. 

      The bus configuration is based on the IMS-1 bus. 

      AzaadiSAT is an 8U Cubesat that weighs around 8 kg. 

      It transports 75 distinct payloads, each weighing roughly 50 grams and performing femto-experiments. 

      These payloads were built with the help of female students from rural areas around the nation. 

      The payloads were assembled by the "Space Kidz India" student team. 

      A UHF-VHF Transponder operating on ham radio frequency to allow amateur radio operators to transmit speech and data, a solid state PIN diode-based Radiation counter to detect the ionizing radiation in its orbit, a long-range transponder, and a selfie camera are among the payloads. 

      The data from this satellite was planned to be received using the ground system built by 'Space Kidz India.'  

      Both satellite missions have failed as a result of the failure of SSLV-D1's terminal stage.



      When Is The SSLV D2 Planned To Lift Off?

      The SSLV's second developmental flight is planned for November of 2022. 

      It is intended to transport four Blacksky Global satellites weighing 56 kg to a 500 km circular orbit with a 50° inclination.  

      It will place the X-ray polarimeter satellite into low Earth orbit(LEO).


      ~ Jai Krishna Ponnappan.


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