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kremlin · 1 year

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forgive me for the questions & any possible inconsistencies or malformations:

how do we compile our symbolic high/low level code into machine language, and how do we find the most efficient method of conversion from symbolic to machine code?

is it possible to include things from asm in C source code?

why do any of the abstractions in C mean anything? how does the compiler translate them?

would it be possible to write and compile a libc in C on a machine which didn't have a precompiled libc? i think the answer is no, so how much peripheral stuff would you need to pull it off?

are these things i could have figured out by just reading the one compiler book with the dragon on the cover?

thanks and apologies.

these are uhh. these are really good questions. like if you're asking these then i think you know way more than you think you do

how do we compile our symbolic high/low level code into machine language?

we do it the natural (only?) way, using parsers, although a bit more technically involved than just a parser. all programming languages are comprised of just keywords (e.g. int, static, enum, if, etc) and operators(+, -, *, %), those are the only reserved terms. everything between them is an identifier (name of variable, struct, etc), so at some level we can just search for the reserved terms and match them, and match whatever is between them as identifiers (var names or whatever). with this, we can tokenize the raw input put it in the context of a defined grammar.

lex is the name of the program that does this. you have simple, defined keywords, and defined operators, and you group them together a few categories (e.g. int, float, long might all fall under the same "variable qualifier" category"). this is tokenization.

those tokens are given to another program, yacc, which deals with it in the context of some context-free defined grammar you've also provided it. this would be a very strict and unambiguous definition of what C code is, defining things like "qualifiers like int and float always proceed an identifier, the addition (+) operator needs two operands, but the ++ (increment by 1) operator only needs one". yacc processes these tokens in the context of the defined grammar, and since those tokens are defined in broad categories, you can start to converge on a method of generating machine code for them.

how do we find the most efficient method of conversion from symbolic to machine code?

ehehehe. ill save this one for later

is it possible to include things from asm in C source code?

yes:

assembly (which translates directly 1-to-1 to machine code) is the only thing a CPU understands, so there has to be a link between the lowest level programming language and assembly at some point. it works about as you'd expect. that asm() block just gets plopped into the ELF right as it appears in the code. there are some features that allow passing C variables in/out of asm() blocks.

why do any of the abstractions in C mean anything? how does the compiler translate them?

hoping above answers this

would it be possible to write and compile a libc in C on a machine which didn't have a precompiled libc? i think the answer is no, so how much peripheral stuff would you need to pull it off?

yes, and in fact, the litmus test that divides "goofy idea some excited kid thought up and posted all over hacker news" and "real programming language" is whether or not that language is bootstrapped, which is whether or not its compiler is written in "itself". e.g. gcc & clang are both C compilers, and they are written in C. bootstrapping is the process of doing this initially. kind of a chicken-and-egg problem, but you just use external things, other languages, or if it is 1970s, direct assembly itself, to create an initial compiler for that language, and the write a compiler for that language in that language, feed it to the intermediate compiler, and bam.

its really hard to do all of this. really hard. lol

are these things i could have figured out by just reading the one compiler book with the dragon on the cover?

idk which one that is

finally...

how do we find the most efficient method of conversion from symbolic to machine code?

so this is kind of an area of debate lol. starting from the bottom, there are some very simple lines of code that directly map to machine code, e.g

a = b + c;

this would just translate to e.g. sparc

add %L1,%L2,%L3 !

if statements would map to branch instructions, etc, etc. pretty straightforward. but there are higher-order optimizations that modern compilers will do, for example, the compiler might see that you write to a variable but never read from it again, and realize since that memory is never read again, you may as well not even bother writing it in the first place, since it won't matter. and then choose to just not include the now-deemed-unnecessary instructions that store whatever values to memory even though you explicitly wrote it in the source code. some of the times this is fine and yields slightly faster code, but other times it results in the buffer you used for your RSA private key not being bzero'd out and me reading it while wearing a balaclava.

#frequently questioned answers

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wildmendergame · 1 year

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How we created the ideal water system for Wildmender

Over the last 4 years of work, we've created a gardening survival game in a desert world that let people create massive and complex oasis where each plant is alive. At the heart of a system-driven, procedurally generated ecology is a water simulation and terraforming system, and this post is to share a bit of how we built it.

We knew early on that the game would include some level of soil and water simulation, just as an outgrowth of wanting to simulate an ecosystem. The early builds used a simple set of flags for soil, which plants could respond to or modify, and had water present only as static objects. Each tile of soil (a 1x1 “meter” square of the game world) could be rock or sand, and have a certain level of fertility or toxicity. Establishing this early put limits on how much we could scale the world, since we knew we needed to store a certain amount of data for each tile.

Since the terrain was already being procedurally generated, letting players shape it to customize their garden was a pretty natural thing to add. The water simulation was added at first in response to this - flat, static bodies of water could easily create very strange results if we let the player dig terrain out from under them. Another neat benefit of this simulation was that it made water a fixed-sum resource - anything the player took out of the ground for their own use wasn’t available for plants, and vice versa. This really resonated well with the whole concept of desert survival and water as a critical resource.

The water simulation at its core is a grid-based solution. Tiles with a higher water level spread it to adjacent tiles in discrete steps. We broke the world up into “simulation cells” (of 32 by 32 tiles each) which let us break things like the water simulation into smaller chunks that we could compute in the background without interrupting the player. The amount of water in each tile is then combined with the height of the underlying terrain to create a water mesh for each simulation cell. Later on, this same simulation cell concept helped us with various optimizations - we could turn off all the water calculations and extra data on cells that didn’t have any water, which is most of the world.

Early on, we were mostly concerned with just communicating what was happening with simple blocks of color - but once the basic simulation worked, we needed to decide how the water should look for the final game. Given the stylized look we were building for the rest of the game, we decided the water should be similarly stylized - the blue-and-white colors made this critical resource stand out to the player more than a more muted, natural, transparent appearance did. White “foam” was added to create clear edges for any body of water (through a combination of screen depth, height above the terrain, and noise.)

We tweaked the water rendering repeatedly over the rest of the project, adding features to the simulation and a custom water shader that relied on data the simulation provided. Flowing water was indicated with animated textures based on the height difference, using a texturing technique called flowmaps. Different colors would indicate clean or toxic water. Purely aesthetic touches like cleaning up the edges of bodies of water, smooth animation of the water mesh, and GPU tessellation on high-end machines got added over time, as well.

The “simulation cell” concept also came into play as we built up the idea of biome transformations. Under the hood, living plants contribute “biomass” to nearby cells, while other factors like wind erosion remove biomass - but if enough accumulates, the cell changes to a new biome, which typically makes survival easier for both plants and players. This system provided a good, organic feel, and it fulfilled one of our main goals of making the player’s home garden an inherently safe and sheltered place - but the way it worked was pretty opaque to players. Various tricks of terrain texturing helped address this, showing changes around plants that were creating a biome transition before that transition actually happened.

As we fleshed out the rest of the game, we started adding new ways to interact with the system we already had. The spade and its upgrades had existed from fairly early, but playtesting revealed a big demand for tools that would help shape the garden at a larger scale. The Earthwright’s Chisel, which allowed the players to manipulate terrain on a larger scale such as digging an entire trench at once, attempted to do this in a way that was both powerful and imprecise, so it didn’t completely overshadow the spade.

We also extended the original biome system with the concept of Mothers and distinct habitats. Mothers gave players more direct control over how their garden developed, in a way that was visibly transformative and rewarding. Giving the ability to create Mothers as a reward for each temple tied back into our basic exploration and growth loops. And while the “advanced” biomes are still generally all better than the base desert, specializing plants to prefer their specific habitats made choosing which biome to create a more meaningful choice.

Water feeds plants, which produce biomass, which changes the biome to something more habitable that loses less water. Plants create shade and block wind-borne threats, which lets other plants thrive more easily. But if those plants become unhealthy or are killed, biomass drops and the whole biome can regress back to desert - and since desert is less habitable for plants, it tends to stay that way unless the player acts to fix it somehow. The whole simulation is “sticky” in important ways - it reinforces its own state, positive or negative. This both makes the garden a source of safety to the player, and allows us to threaten it - with storms, wraiths, or other disasters - in a way that demands players take action.

#indie games #wildmender #gaming #gardening #devblog

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seoblog4 · 3 months

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Real-Time Symmetrical Fault Monitoring and Control

Symmetrical faults, power stability analysis also known as balanced or three-phase faults, occur when all three phases of an electrical power system experience a short-circuit simultaneously. These types of faults can lead to significant disruptions in power supply, equipment damage, and potential safety hazards. Effective real-time monitoring and control of symmetrical faults is crucial for the reliable and safe operation of power systems.

Symmetrical Fault Characteristics

Symmetrical faults are characterized by the following:

Equal Magnitude Fault Currents: During a symmetrical fault, the fault currents in all three phases are equal in magnitude.

Balanced Fault Currents: The fault currents in the three phases are balanced, meaning that the vector sum of the three fault currents is zero.

Balanced Voltages: The voltages at the fault location are also balanced, with the three phase voltages being equal in magnitude and 120 degrees apart.

These unique characteristics of symmetrical faults require specialized monitoring and control techniques to effectively manage the system under fault conditions.

Real-Time Monitoring Approach

Effective real-time monitoring of symmetrical faults involves the following key components:

Synchronized Phasor Measurement Units (PMUs): PMUs provide highly accurate and time-synchronized measurements of voltage and current phasors across the power system. This data is essential for analyzing the system's behavior during a symmetrical fault.

Fault Detection and Classification Algorithms: Advanced algorithms are used to rapidly detect the occurrence of a symmetrical fault and distinguish it from other types of faults or system disturbances.

State Estimation and Fault Location Techniques: Sophisticated state estimation and fault location algorithms utilize the PMU data to accurately identify the location and characteristics of the symmetrical fault.

Real-Time Control Strategies

Once a symmetrical fault is detected and its characteristics are determined, the following real-time control strategies can be implemented:

Fault Isolation: Rapidly identifying and isolating the faulted section of the power system is crucial to minimize the impact on the rest of the network.

Load Shedding: If necessary, selective load shedding can be initiated to reduce the system load and prevent further deterioration of the power quality.

Generation Reconfiguration: Adjusting the output of generating units or activating backup generators can help maintain system stability and restore power supply.

Reactive Power Compensation: Dynamic reactive power compensation devices, such as static VAR compensators (SVCs) or static synchronous compensators (STATCOMs), can be employed to regulate voltage levels and improve system stability.

Effective real-time monitoring and control of symmetrical faults is crucial for the reliable and safe operation of modern power systems. By leveraging advanced technologies like PMUs,symmetrical fault analysis in power system sophisticated algorithms, and coordinated control strategies, power system operators can quickly identify, isolate, and mitigate the impact of symmetrical faults, ensuring the continued delivery of high-quality electrical power to consumers.

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midseo · 4 months

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Power Control Centre Panels, MCC Panels, Manufacturer, Kolhapur, India

We are Manufacturer, Supplier, Exporter of Power Control Centre Panels, MCC Panels, APFC Panels, PCC Panels, SVG Panels, PDB Panels from Kolhapur, India.

Power Control Centre Panels, MCC Panels, SVG Panels, PDB Panels, Static Var Generator Panels, PCC Panels, APFC Panels, Power Distribution Board Panels, Motor Control Center Panels, Manufacturer, Supplier, Exporter, Kolhapur, Maharashtra, India

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kaichpower · 5 months

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It's loaded and shipped. If any friend is interested in our products can contact me!

#electrical products #power #electrical #supplier #svg

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fgi-inverter · 5 months

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The world's top 500 FGI Technology Co., LTD., with its deep accumulation and continuous innovation in the field of power electronics technology, has successfully created a series of industry-leading high voltage inverter and FDSVG Static Var Generator, occupying a prominent position in the international market.

#inverter，SVG

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brilworks · 10 months

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Java vs Kotlin

Java is an incredibly popular and versatile programming language that is used in web, mobile, and desktop application development. On the other hand, Kotlin is a newer, more advanced version of Java, and it has a bunch of modern features that Java doesn’t have. However, if you are used to Java, there are several things you can miss in Kotlin.

In this article, we will compare the syntax, features, performance, and cost associated with developing programs in both languages so you can understand which language is suitable for your project.

Introduction

Java is a versatile, mature, platform-independent, general-purpose programming language that has maintained its prominence for more than two decades. Kotlin is a newer programming language inspired by Java, with a handful of modern features that help developers write programs in a more concise way.

Both languages are used in the development of a variety of applications. However, there are some scenarios where you may prefer one over the other, which we will discuss in a later part of this article. For example, Kotlin has an upper in Android application development while Java is still dominating the server-side, web, and desktop applications.

In fact, Kotlin was developed to address some of the limitations in Java, which has contributed to its growing popularity. Since it is fully interoperable with Java (you can use existing Java libraries, APIs, frameworks, and code and even mix both in one project), it also adds to the functionalities of Java.

Considering these factors, the debate among developers has consistently revolved around Java vs Kotlin. While no programming language can definitively claim superiority, a particular language may prove more suitable once your specific requirements become clear.

Let’s explore some key factors that deserve your attention to help you make an informed decision.

Syntax: Java vs. Kotlin

Kotlin is like a concise poem, while Java is like a verbose essay.

Kotlin is designed to support both object-oriented and functional programming paradigms, which allows developers to write code that is both expressive and concise. Additionally, Kotlin offers a more concise syntax.

Here is a simple program to calculate the sum in Java and Kotlin that compares the conciseness of the two languages:

In Java

public class JavaProgram { public static void main(String[] args) { int sum = 0; for (int i = 0; i < 10; i++) { sum += i; } System.out.println("The sum is: " + sum); } }

In Kotlin

fun main() { var sum = 0 for (i in 0 until 10) { sum += i } println("The sum is: $sum") }

Both programs perform identical tasks, but it’s the Kotlin program that glides gracefully with a touch of readability. For example, in Kotlin’s for loop, it doesn’t just use the mundane “for" keyword and the mundane "i < 10" condition – it has "until" keyword, making the program more human readable.

In addition to this, there is a wealth of improvement Kotlin has to offer for Java developers, including a few of them, such as you don’t need to explicitly specify the data type, Kotlin’s function declaration is shorter, etc.

On the other hand, Java requires variables and expressions to be declared explicitly. Therefore, Java has a relatively verbose syntax, with many keywords and punctuation marks.

Kotlin is also a statically typed language, but it has a more concise syntax than Java. Kotlin uses type inference to automatically determine the types of variables and expressions, which can make code more readable and maintainable.

In terms of writing programs, Kotlin has the upper hand, and you can express the same functionality with less code in a concise way.

Comparison: Java vs. Kotlin

Features: Java vs. Kotlin

More specifically, Kotlin is a newer language with modern features compared to its counterpart, Java. They share many common features, but there are also several distinct features that developers often evaluate from their perspective.

Features such as Explicit nullability and Extension functions help developers write more reliable and maintainable codebases in Kotlin. On the other hand, Kotlin does not have the ternary operator, static members, checked exceptions, and protected features.

Checked exceptions: Java requires developers to handle checked exceptions explicitly. This helps to prevent errors.

Higher-order functions: These functions can take other functions as arguments or return functions as results. This can be useful for writing more concise and reusable code.

Static members: Static members are members of a class that can be accessed without creating an instance of the class. This can be useful for sharing code and data.

Strictfp: The Strictfp keyword can be used to declare a method as having strict floating-point semantics. This can be useful for some specific tasks, but it can also make code less efficient.

Data classes: Data classes are a simple way to create immutable data classes. This can be useful for writing more concise and efficient code.

Ternary operator: Ternary operator is a shorthand way to write conditional expressions. This can be useful for writing more concise code.

Coroutines: Coroutines are a lightweight way to write asynchronous code. This can be useful for writing more responsive and efficient code.

Varargs: Varargs is a feature that allows a method to accept a variable number of arguments. This can be useful for writing more concise and flexible code.

Native methods: Native methods are methods that are implemented in a native language, such as C or C++. This can be useful for writing code that is more efficient, or that needs to interact with native code.

Extension functions: Extension functions can be added to existing classes without having to modify the classes themselves. This can be useful for adding new functionality to existing classes without having to make breaking changes.

Type inference: Type inference is a feature that allows Kotlin to infer the types of variables and expressions. This can make code more concise and easier to read.

Smart casts: Smart casts are a feature that allows Kotlin to cast variables to their inferred types automatically, making code more concise and easier to read.

Inline functions: Inline functions are functions that are expanded inline at the point of use. This can improve performance, but it can also make code more difficult to read.

Type aliases: Type aliases are a way to create new names for existing types. This can be useful for making code more concise and readable.

Null safety: Among these features, Null safety is a great addition to Kotlin, which saves time for developers as developers don’t need to deal with null NullPointerException. They are a significant problem in Java projects.

Performance: Kotlin vs. Java

They both have similar performance, as they are compiled to bytecode and run on the JVM. Kotlin may have a slight advantage over Java in certain cases. However, Java code is typically compiled faster than Kotlin, especially when using reflection.

On the other hand, Kotlin may outperform Java for small and simple programs, as some benchmarks have shown that Kotlin has better performance than Java in these cases. Additionally, Kotlin’s unique features, such as null safety and immutable data structures, can also contribute to the performance of Kotlin programs.

NullPointerException can reduce the number of runtime exceptions, resulting in improved performance of the application.

In summary, choose Java if you need to develop large and complex applications, as it is a mature language with robust resources. Kotlin is a newer language, but it is taking over Java for small application development due to its concise syntax and modern features.

Community Support: Java vs. Kotlin

Java

Java has been around for a long time and has a massive community. This translates into a wealth of resources, libraries, frameworks, and essential tools for developers. You can find solutions to almost any issues that you may encounter.

Additionally, Java has a great ecosystem of tools and IDEs, like Eclipse and IntelliJ IDEA, backed by a robust community of contributors.

Kotlin

Kotlin is relatively new, but its community is growing rapidly. You will also find many libraries and several frameworks available for Kotlin development, and support is available from Google and other companies. Kotlin’s official website, along with online communities like Stack Overflow and GitHub, provides ample resources for learning and troubleshooting.

Read more: https://www.brilworks.com/blog/java-vs-kotlin/

#java #java development company #kotlin #java developers #kotlin app development

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bharat059 · 1 year

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Static VAR Generator Market Size, Share & Industry Analysis, By Type (Thyristor Based, MCR Based), By End-User (Electric Utility, Renewable, Railway, Oil & Gas, Steel and Mining Industry, Others) and Regional Forecast, 2022-2029

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inventumpower · 2 years

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Reduce Energy Costs with Inventum Power's Static Var

Improve Your Power Factor and Reduce Energy Costs with Inventum Power's Static Var Generator. Our advanced technology will optimize your power usage and improve the efficiency of your electrical systems. Contact us now to learn more!

For any queries:- 📩[email protected] 📲9716667972, 9650334786 🌐https://www.inventumpower.com/

#static var generator

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electronalytics · 1 year

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Power Transmission Lines and Towers Market Promising Growth and by Platform Type, Technology and Outlook by 2032

Overview of the Market:

Power Transmission Lines and Towers Market Overview: The power transmission lines and towers market plays a crucial role in the global energy industry by facilitating the efficient transmission of electrical power from generation sources to distribution networks. This market has experienced promising growth in recent years, driven by increasing electricity demand, renewable energy integration, and grid modernization initiatives.

Power Transmission Lines and Towers Market is likely to register a valuation of US$ 30 billion in 2023 and reach US$ 63.6 billion by 2033, at a CAGR of 7.8% (2023-2033).

Promising Growth and Demand: The power transmission lines and towers market has witnessed significant growth due to various factors. The increasing need for reliable and secure electricity transmission, expanding urbanization, industrialization, and infrastructural development in emerging economies are driving the demand for new transmission lines and towers. Furthermore, the integration of renewable energy sources, such as solar and wind power, into the grid requires additional transmission infrastructure to transport electricity from remote generation sites to load centers.

Platform Type: The power transmission lines and towers market can be categorized into two main platform types:

Overhead Transmission Lines and Towers: Overhead transmission lines utilize towers or poles to carry high-voltage power cables suspended above the ground. These lines are commonly used for long-distance transmission and are cost-effective for traversing varied terrains.

Underground Transmission Lines and Substations: Underground transmission lines are installed beneath the ground, typically in densely populated urban areas or environmentally sensitive regions. These lines require specialized cables and equipment, including underground substations, to manage the flow of electricity.

Technology: Several technologies are employed in power transmission lines and towers to enhance efficiency, reliability, and safety:

High-Voltage Direct Current (HVDC) Transmission: HVDC technology enables the long-distance transmission of electricity with reduced losses compared to traditional alternating current (AC) transmission. It is commonly used for interconnecting grids and integrating renewable energy sources.

Flexible AC Transmission Systems (FACTS): FACTS technologies improve the transmission system's stability, capacity, and power quality. These systems include devices such as static var compensators (SVCs), static synchronous compensators (STATCOMs), and thyristor-controlled series compensators (TCSCs).

Smart Grid Technologies: Smart grid technologies integrate digital communication and advanced monitoring systems into the power transmission network. These technologies enhance grid resilience, optimize power flow, enable real-time monitoring, and facilitate demand response programs.

End User Industry: The power transmission lines and towers market caters to various end user industries, including:

Utilities: Electric utilities and power generation companies are major consumers of transmission lines and towers. They require reliable infrastructure to transmit electricity from power plants to distribution networks and end users.

Renewable Energy Sector: The integration of renewable energy sources, such as solar and wind power, into the grid necessitates additional transmission infrastructure. Transmission lines and towers are crucial for transporting renewable energy from remote generation sites to population centers.

Industrial Sector: Industries with high power demand, such as manufacturing facilities, mining operations, and data centers, rely on robust transmission lines and towers for a stable and uninterrupted power supply.

Commercial and Residential Sector: Transmission lines and towers deliver electricity to commercial buildings, residential areas, and households. Reliable transmission infrastructure ensures a consistent power supply for businesses and consumers.

Scope:

The power transmission lines and towers market has a global scope, with increasing investments in grid expansion, interconnections, and modernization projects across regions. The market encompasses various stakeholders, including transmission line manufacturers, tower fabricators, equipment suppliers, engineering firms, utilities, and regulatory bodies.

Market statistics, growth projections, and scope may vary depending on specific regions and market dynamics. However, the power transmission lines and towers market is expected to witness continued growth in the coming years, driven by the need for reliable and efficient electricity transmission.

In conclusion, the power transmission lines and towers market is experiencing promising growth worldwide due to factors such as increasing electricity demand, renewable energy integration, and grid modernization initiatives. The market includes overhead and underground transmission lines, utilizes technologies like HVDC and FACTS, and caters to diverse end user industries. The demand for reliable transmission infrastructure is expected to drive the market's growth, presenting opportunities for industry players in the global energy sector.

We recommend referring our Stringent datalytics firm, industry publications, and websites that specialize in providing market reports. These sources often offer comprehensive analysis, market trends, growth forecasts, competitive landscape, and other valuable insights into this market.

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Market Segmentations:

Global Power Transmission Lines and Towers Market: By Company • Siemens • ABB • GE • EMC • K-Line • ICOMM • CG • KEC • Aurecon • Arteche • Mastec • Sterling & Wilson Global Power Transmission Lines and Towers Market: By Type • High Tension • Extra High Tension • Ultra High Tension Global Power Transmission Lines and Towers Market: By Application • Energy • Industrial • Military & Defense • Others Global Power Transmission Lines and Towers Market: Regional Analysis The regional analysis of the global Power Transmission Lines and Towers market provides insights into the market's performance across different regions of the world. The analysis is based on recent and future trends and includes market forecast for the prediction period. The countries covered in the regional analysis of the Power Transmission Lines and Towers market report are as follows: North America: The North America region includes the U.S., Canada, and Mexico. The U.S. is the largest market for Power Transmission Lines and Towers in this region, followed by Canada and Mexico. The market growth in this region is primarily driven by the presence of key market players and the increasing demand for the product. Europe: The Europe region includes Germany, France, U.K., Russia, Italy, Spain, Turkey, Netherlands, Switzerland, Belgium, and Rest of Europe. Germany is the largest market for Power Transmission Lines and Towers in this region, followed by the U.K. and France. The market growth in this region is driven by the increasing demand for the product in the automotive and aerospace sectors. Asia-Pacific: The Asia-Pacific region includes Singapore, Malaysia, Australia, Thailand, Indonesia, Philippines, China, Japan, India, South Korea, and Rest of Asia-Pacific. China is the largest market for Power Transmission Lines and Towers in this region, followed by Japan and India. The market growth in this region is driven by the increasing adoption of the product in various end-use industries, such as automotive, aerospace, and construction. Middle East and Africa: The Middle East and Africa region includes Saudi Arabia, U.A.E, South Africa, Egypt, Israel, and Rest of Middle East and Africa. The market growth in this region is driven by the increasing demand for the product in the aerospace and defense sectors. South America: The South America region includes Argentina, Brazil, and Rest of South America. Brazil is the largest market for Power Transmission Lines and Towers in this region, followed by Argentina. The market growth in this region is primarily driven by the increasing demand for the product in the automotive sector.

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#Power Transmission Lines and Towers Market Promising Growth and by Platform Type #Technology and Outlook by 2032

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sgurrenergy11 · 1 year

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Capacitor Banks or STATCOM for better Power Factor Correction

Reactive power compensation is an essential aspect of electrical power systems. Reactive power is the power that flows back and forth between the source and load without doing any useful work. It is required to maintain the voltage level in the system and to create the magnetic field necessary for the operation of motors, transformers, and other inductive loads.

However, a high level of reactive power can result in several problems in the power system, including:

Reduced efficiency: A high level of reactive power can reduce the overall efficiency of the power system. This is because the transmission lines and transformers have to handle more current to deliver the same amount of power, resulting in higher losses.

Voltage drop: Reactive power causes voltage drops in the system, which can lead to reduced performance of electrical equipment and can cause equipment to malfunction or fail.

Low power factor: A low power factor means that a high proportion of the power supplied to the load is not being used for useful work, resulting in increased energy costs.

Reactive power compensation can help mitigate these problems by balancing the reactive power in the system. It involves the use of equipment such as capacitors, reactors, and static VAR compensators (SVCs) to supply or absorb reactive power as required to maintain a stable voltage and power factor.

By compensating for reactive power, the power system can operate more efficiently, with improved voltage regulation, reduced power losses, and increased capacity. It can also help to reduce electricity bills, improve power quality, and reduce the environmental impact of power generation.

As per the MOM released by CEA on 21st April 2023 for the meeting held on 13th April 2023. It was mentioned that there were 28 incidents involving loss of more than 1000 MW RE generation in the grid since January 2022. The grid events that occurred are categorized into three main categories.

Overvoltage during switching operation

Fault in vicinity of RE complex

Low frequency oscillations in RE complex

The analysis of these grid events revealed that both under the steady and dynamic states, varying reactive power support from VRE was found to be one of the contributing factors.

As the requirement of dynamic reactive power compensation is very clearly mentioned in the Technical Standards for Connectivity to the Grid, (Amendment), regulations, 2012-clause B2-1-published on dated 15th October 2013.

Clause B2-1 mentioned that ‘’the generating station shall be capable of supplying dynamically varying reactive power support so as to maintain power factor within the limits of 0.95 lagging to 0.95 leading’’.

In MOM it is mentioned that the above provision of the CEA connectivity regulations was not being complied in totality. Grid-India submitted that the dynamically varying reactive support is necessary during transient conditions such as LVRT or HVRT and also it was explained that the fixed capacitor banks can provide reactive power support only during steady state and also the support is delivered in steps after time delay.

The transient effects associated with the activation of a capacitor bank can have a significant impact on the stability and performance of the power system. Considering the above-mentioned facts and the advantages listed below, SgurrEnergy recommends the use of dynamic reactive power compensation using additional Inverters, STATCOM, or SVG over the use of capacitor banks.

Advantages over capacitor banks-

Dynamic response: Inverters, STATCOM or SVG can respond to changes in the power factor much faster than capacitor banks. This is because they use power electronics to adjust the reactive power, while capacitor banks have a fixed reactive power value.

No resonance issues: Capacitor banks can create resonance issues when the frequency of the system is close to the resonance frequency. This device does not have this issue as it operates on a different principle.

Wide operating range: Inverters, STATCOM or SVG this device can operate over a wide range of reactive power and voltage levels, while capacitor banks have limited operating ranges.

Harmonic suppression: Inverters, STATCOM or SVG can also suppress harmonics, which are undesirable distortions in the power system. Capacitor banks do not have this capability.

Smaller size: SVG is generally smaller in size compared to capacitor banks, which can be important in space-constrained applications.

Overall, the mentioned technology (Inverters, STATCOM or SVG) is a more versatile and efficient technology compared to capacitor banks for power factor correction and other related applications.

#renewableenergy #greenenergy #power #inverter #electricity

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codehunter · 1 year

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Exception gevent.hub.LoopExit: LoopExit('This operation would block forever',)

I am always getting this error when running my Flask App with Websockets.I have tried to follow this guide - http://blog.miguelgrinberg.com/post/easy-websockets-with-flask-and-gevent

I have a flask app that provides the GUI interface for my network sniffer. The sniffer is inside a thread as shown below : ( l is the thread for my sniffer; isRunning is a boolean to check if the thread is already running)

try: if l.isRunning == False: # if the thread has been shut down l.isRunning = True # change it to true, so it could loop again running = True l.start() # starts the forever loop / declared from the top to be a global variable print str(running) else: running = True print str(running) l.start()except Exception, e: raise ereturn flask.render_template('test.html', running=running) #goes to the test.html page

The sniffer runs fine without the socketio and i am able to sniff the network while traversing my gui.However, when I included the socketio in the code, I first see the socketio working in my index page and i am able to receive the messages from the server to the page. I could also traverse fine to the other static paages in my GUI ;however, activating my threaded network sniffer would leave my browser hangup. I always get the Exception gevent.hub.LoopExit: LoopExit('This operation would block forever',) error and when I rerun my program, the console would say that the address is already in use. It seems to me that I may not be closing my sockets correctly as this is happening. I also think that some operation is blocking based from the error. The code in my python flask app is shown below

def background_thread(): """Example of how to send server generated events to clients.""" count = 0 while True: time.sleep(10) count += 1 socketio.emit('my response',{'data': 'Server generated event', 'count': count},namespace='/test')if socketflag is None: thread = Thread(target=background_thread) thread.start()@socketio.on('my event', namespace='/test')def test_message(message): emit('my response', {'data': message['data']})@socketio.on('my broadcast event', namespace='/test')def test_message(message): emit('my response', {'data': message['data']}, broadcast=True)@socketio.on('connect', namespace='/test')def test_connect(): emit('my response', {'data': 'Connected'})@socketio.on('disconnect', namespace='/test')def test_disconnect(): print('Client disconnected')

Here is the code present in my index page.

https://codehunter.cc/a/flask/exception-gevent-hub-loopexit-loopexit-this-operation-would-block-forever

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