Siemens – Siemens and Rittal partner to design data center infrastructure of the future

EMR Analysis

More information on Siemens AG: See full profile on EMR Executive Services

More information on Dr. Roland Busch (President and Chief Executive Officer, Siemens AG): See full profile on EMR Executive Services

More information on Prof. Dr. Ralf P. Thomas (Member of the Managing Board and Chief Financial Officer, Siemens AG till May 13, 2026 + Special Advisor to the Supervisory Board and the Managing Board, Siemens AG as from May 13, 2026 till end of 2026 + Chairman of the Supervisory Board, Siemens Healthineers AG, Siemens AG): See full profile on EMR Executive Services

More information on Veronika Bienert (Member of the Managing Board and Chief Financial Officer, Siemens AG as from April 1, 2026): See full profile on EMR Executive Services

More information on “ONE Tech Company” Program by Siemens AG: See full profile on EMR Executive Services

 

More information on Siemens Smart Infrastructure (SI) by Siemens AG: See the full profile on EMR Executive Services

More information on Matthias Rebellius (Member of the Managing Board and Chief Executive Officer, Siemens Smart Infrastructure (SI), Siemens AG till July 1, 2026 + Member of the Supervisory Board, Siemens Energy AG): See the full profile on EMR Executive Services

More information on Dr. Peter Körte (Member of the Managing Board and Chief Technology and Chief Strategy Officer with responsibility for Siemens Xcelerator and Siemens Advanta, Siemens AG + Member of the Managing Board and Chief Executive Officer, Siemens Smart Infrastructure (SI), Siemens AG as from July 1, 2026): See the full profile on EMR Executive Services

More information on Andreas Matthé (Chief Executive Officer, Electrical Products, Siemens Smart Infrastructure (SI), Siemens AG): See the full profile on EMR Executive Services

 

 

 

More information on The Friedhelm Loh Group: See the full profile on EMR Executive Services

More information on Prof. Dr.-Ing E.H. Friedhelm Loh (Owner, Chairman and Chief Executive Officer, The Friedhelm Loh Group): See the full profile on EMR Executive Services

More information on Ralph Lindackers (Member of the Board of Directors and Chief Financial Officer, The Friedhelm Loh Group): See the full profile on EMR Executive

 

More information on Rittal by The Friedhelm Loh Group: See the full profile on EMR Executive Services

More information on Prof. Dr. Niko Mohr (Member of the Executive Board, The Friedhelm Loh Group + Member of the Board of Directors and Chief Executive Officer, Rittal International and Rittal Software Systems, The Friedhelm Loh Group): See the full profile on EMR Executive

 

More information on RiLineX by Rittal by The Friedhelm Loh Group: https://www.rittal.com/com-en/Solutions/Energy-Solutions/RiLineX + RiLineX is the new 60 mm busbar system platform for power distribution in industry, buildings and IT infrastructure – manufacturer-neutral and licence-free. This makes it suitable for use in almost all sectors because it adapts individually to virtually any requirement. The standardised boards may be flexibly extended as needed. The fully integrated contact hazard protection to IP 2XB, upgradable to IP 4X, ensures maximum protection and minimises the level of product and system expertise needed for planning and installation. In this way, RiLineX sets new standards in power distribution, helping to future-proof your company for the challenges of the energy sector.

More information on Ri4Power by Rittal by The Friedhelm Loh Group: https://www.rittal.com/com-en/products/PG20231215POW101/PG20240417STR001 + VX25 Ri4Power – The switchgear and power distribution system for the VX25. The VX25 Ri4Power allows you to create different sections within a switchgear or power distribution system to perform these tasks. System solutions for circuit-breakers from well-known manufacturers. Ideal for compact distribution of electrical power.

 

 

 

More information on International Electrotechnical Commission (IEC): https://www.iec.ch/homepage + The IEC (International Electrotechnical Commission) brings together 170 countries, and more than 20,000 experts cooperate on the global IEC platform to ensure that products work everywhere safely with each other. 

The IEC is the world’s leading organization that prepares and publishes globally relevant International Standards for the whole energy chain, including all electrical, electronic and related technologies, devices and systems. The IEC also supports all forms of conformity assessment and administers four Conformity Assessment Systems that certify that components, equipment and systems used in homes, offices, healthcare facilities, public spaces, transportation, manufacturing, explosive environments and energy generation conform to them. 

IEC work covers a vast range of technologies: power generation (including all renewable energy sources), transmission, distribution, Smart Grid, batteries, home appliances, office and medical equipment, all public and private transportation, semiconductors, fibre optics, nanotechnology, multimedia, information technology, and more. It also addresses safety, EMC, performance and the environment.

More information on Jim Matthews (President 2026-2028, International Electrotechnical Commission (IEC)): https://www.iec.ch/leadership + https://www.linkedin.com/in/jim-matthews-a08b261b4/ 

 

 

 

More information on OCP (Open Compute Project): https://www.opencompute.org/ + The Open Compute Project Foundation (OCP) was initiated in 2011 with a mission to apply the benefits of open source and open collaboration to hardware and rapidly increase the pace of innovation in, near and around the data center.

The Open Compute Project (OCP) is a collaborative community focused on redesigning hardware technology to efficiently support the growing demands on compute infrastructure.

OCP’s collaboration model is being applied beyond the data center, helping to advance the telecom industry & EDGE infrastructure.

More information on George Tchaparian (Chief Executive Officer, OCP): https://www.opencompute.org/about/foundation-staff + https://www.linkedin.com/in/george-tchaparian-b1640a1/ 

 

 

 

 

 

 

 

 

 

 

 

EMR Additional Notes:

• Cloud Computing:
  • Cloud computing is a general term for anything that involves delivering hosted services over the internet. It is the on-demand availability of computer system resources, especially data storage and computing power, without direct active management by the user. Large clouds often have functions distributed over multiple locations, each location being a data center.
• Edge Computing:
  • Edge computing is a form of computing that is done on site or near a particular data source, minimizing the need for data to be processed in a remote data center.
  • Edge computing can enable more effective city traffic management. Examples of this include optimising bus frequency given fluctuations in demand, managing the opening and closing of extra lanes, and, in future, managing autonomous car flows.
  • An edge device is any piece of hardware that controls data flow at the boundary between two networks. Edge devices fulfill a variety of roles, depending on what type of device they are, but they essentially serve as network entry — or exit — points.
  • There are five main types of edge computing devices: IoT sensors, smart cameras, uCPE equipment, servers and processors. IoT sensors, smart cameras and uCPE equipment will reside on the customer premises, whereas servers and processors will reside in an edge computing data centre.
  • In service-based industries such as the finance and e-commerce sector, edge computing devices also have roles to play. In this case, a smart phone, laptop, or tablet becomes the edge computing device.
  • Edge Devices:
    • Edge devices encompass a broad range of device types, including sensors, actuators and other endpoints, as well as IoT gateways. Within a local area network (LAN), switches in the access layer — that is, those connecting end-user devices to the aggregation layer — are sometimes called edge switches.

 

• Hybrid Computing: 
  • A hybrid cloud integrates private, on-premises infrastructure with public cloud services, offering flexibility to distribute workloads between these environments. Hybrid models often incorporate edge computing, allowing organizations to run critical workloads locally at the edge while using the cloud for other tasks, thereby optimizing performance, cost, and data management for various business needs.
• HPC (Hight-Performance Computing):
  • Practice of aggregating computing resources to gain performance greater than that of a single workstation, server, or computer. HPC can take the form of custom-built supercomputers or groups of individual computers called clusters.

 

• Data Centers:
  • A data center is a facility that centralizes an organization’s shared IT operations and equipment for the purposes of storing, processing, and disseminating data and applications. Because they house an organization’s most critical and proprietary assets, data centers are vital to the continuity of daily operations.
• Hyperscale Data Centers:
  • The clue is in the name: hyperscale data centers are massive facilities built by companies with vast data processing and storage needs. These firms may derive their income directly from the applications or websites the equipment supports, or sell technology management services to third parties.
• White Space and Grey Space in Data Centers:
  • White space in a data center refers to the area where IT equipment is placed. It typically houses servers, storage, network gear, and racks.
  • Gray space, on the other hand, is the area where the back-end infrastructure is located. This space is essential for supporting the IT equipment and includes areas for switchgear, UPS, transformers, chillers, and generators.

 

• Edge & Cloud Services: 
  • Edge services perform data processing on local devices and servers near the data source, reducing latency for time-sensitive operations, while cloud services centralize large computations and storage in remote datacenters, offering massive scalability and flexibility for general workloads.
  • Most organizations use both, creating an “edge-to-cloud” architecture where edge devices handle immediate tasks, and the cloud manages large-scale data processing and complex applications, providing a seamless and efficient experience.

 

 

• AI – Artificial Intelligence:
  • Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems.
  • As the hype around AI has accelerated, vendors have been scrambling to promote how their products and services use AI. Often what they refer to as AI is simply one component of AI, such as machine learning. AI requires a foundation of specialized hardware and software for writing and training machine learning algorithms. No one programming language is synonymous with AI, but several, including Python, R and Java, are popular.
  • In general, AI systems work by ingesting large amounts of labeled training data, analyzing the data for correlations and patterns, and using these patterns to make predictions about future states. In this way, a chatbot that is fed examples of text chats can learn to produce lifelike exchanges with people, or an image recognition tool can learn to identify and describe objects in images by reviewing millions of examples.
  • AI programming focuses on three cognitive skills: learning, reasoning and self-correction.
  • The 4 types of artificial intelligence?
    • Type 1: Reactive machines. These AI systems have no memory and are task specific. An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on the chessboard and make predictions, but because it has no memory, it cannot use past experiences to inform future ones.
    • Type 2: Limited memory. These AI systems have memory, so they can use past experiences to inform future decisions. Some of the decision-making functions in self-driving cars are designed this way.
    • Type 3: Theory of mind. Theory of mind is a psychology term. When applied to AI, it means that the system would have the social intelligence to understand emotions. This type of AI will be able to infer human intentions and predict behavior, a necessary skill for AI systems to become integral members of human teams.
    • Type 4: Self-awareness. In this category, AI systems have a sense of self, which gives them consciousness. Machines with self-awareness understand their own current state. This type of AI does not yet exist.
  • Machine Learning (ML):
    • Developed to mimic human intelligence, it lets the machines learn independently by ingesting vast amounts of data, statistics formulas and detecting patterns.
    • ML allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.
    • ML algorithms use historical data as input to predict new output values.
    • Recommendation engines are a common use case for ML. Other uses include fraud detection, spam filtering, business process automation (BPA) and predictive maintenance.
    • Classical ML is often categorized by how an algorithm learns to become more accurate in its predictions. There are four basic approaches: supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning.
  • Deep Learning (DL):
    • Subset of machine learning, Deep Learning enabled much smarter results than were originally possible with ML. Face recognition is a good example.
    • DL makes use of layers of information processing, each gradually learning more and more complex representations of data. The early layers may learn about colors, the next ones about shapes, the following about combinations of those shapes, and finally actual objects. DL demonstrated a breakthrough in object recognition.
    • DL is currently the most sophisticated AI architecture we have developed.
  • Generative AI (GenAI):
    • Generative AI technology generates outputs based on some kind of input – often a prompt supplied by a person. Some GenAI tools work in one medium, such as turning text inputs into text outputs, for example. With the public release of ChatGPT in late November 2022, the world at large was introduced to an AI app capable of creating text that sounded more authentic and less artificial than any previous generation of computer-crafted text.
  • Small Language Models (SLM) and Large Language Models (LLM):
    • Small Language Models (SLMs) are artificial intelligence (AI) models capable of processing, understanding and generating natural language content. As their name implies, SLMs are smaller in scale and scope than large language models (LLMs).
    • LLM means Large Language Models — a type of machine learning/deep learning model that can perform a variety of natural language processing (NLP) and analysis tasks, including translating, classifying, and generating text; answering questions in a conversational manner; and identifying data patterns.
    • For example, virtual assistants like Siri, Alexa, or Google Assistant use LLMs to process natural language queries and provide useful information or execute tasks such as setting reminders or controlling smart home devices.
  • Computer Vision (CV) / Vision AI & Machine Vision (MV):
    • Field of AI that enables computers to interpret and act on visual data (images, videos). It works by using deep learning models trained on large datasets to recognize patterns, objects, and context.
    • The most well-known case of this today is Google’s Translate, which can take an image of anything — from menus to signboards — and convert it into text that the program then translates into the user’s native language.
    • Machine Vision (MV) :
      • Specific application for industrial settings, relying on cameras to analyze tasks in manufacturing, quality control, and worker safety. The key difference is that CV is a broader field for extracting information from various visual inputs, while MV is more focused on specific industrial tasks.
      • Machine Vision is the ability of a computer to see; it employs one or more video cameras, analog-to-digital conversion and digital signal processing. The resulting data goes to a computer or robot controller. Machine Vision is similar in complexity to Voice Recognition.
      • MV uses the latest AI technologies to give industrial equipment the ability to see and analyze tasks in smart manufacturing, quality control, and worker safety.
  • Multimodal Intelligence and Agents:
    • Subset of artificial intelligence that integrates information from various modalities, such as text, images, audio, and video, to build more accurate and comprehensive AI models.
    • Multimodal capabilities allows AI to interact with users in a more natural and intuitive way. It can see, hear and speak, which means that users can provide input and receive responses in a variety of ways.
    • An AI agent is a computational entity designed to act independently. It performs specific tasks autonomously by making decisions based on its environment, inputs, and a predefined goal. What separates an AI agent from an AI model is the ability to act. There are many different kinds of agents such as reactive agents and proactive agents. Agents can also act in fixed and dynamic environments. Additionally, more sophisticated applications of agents involve utilizing agents to handle data in various formats, known as multimodal agents and deploying multiple agents to tackle complex problems.
  • Agentic AI:
    • Agentic AI is an artificial intelligence system that can accomplish a specific goal with limited supervision. It consists of AI agents—machine learning models that mimic human decision-making to solve problems in real time. In a multiagent system, each agent performs a specific subtask required to reach the goal and their efforts are coordinated through AI orchestration.
    • Unlike traditional AI models, which operate within predefined constraints and require human intervention, agentic AI exhibits autonomy, goal-driven behavior and adaptability. The term “agentic” refers to these models’ agency, or, their capacity to act independently and purposefully.
    • Agentic AI builds on generative AI (gen AI) techniques by using large language models (LLMs) to function in dynamic environments. While generative models focus on creating content based on learned patterns, agentic AI extends this capability by applying generative outputs toward specific goals.
  • Edge AI Technology:
    • Edge artificial intelligence refers to the deployment of AI algorithms and AI models directly on local edge devices such as sensors or Internet of Things (IoT) devices, which enables real-time data processing and analysis without constant reliance on cloud infrastructure.
    • Simply stated, edge AI, or “AI on the edge“, refers to the combination of edge computing and artificial intelligence to execute machine learning tasks directly on interconnected edge devices. Edge computing allows for data to be stored close to the device location, and AI algorithms enable the data to be processed right on the network edge, with or without an internet connection. This facilitates the processing of data within milliseconds, providing real-time feedback.
    • Self-driving cars, wearable devices, security cameras, and smart home appliances are among the technologies that leverage edge AI capabilities to promptly deliver users with real-time information when it is most essential.
  • High-Density AI: 
    • High-density AI refers to the concentration of AI computing power and storage within a compact physical space, often found in specialized data centers. This approach allows for increased computational capacity, faster training times, and the ability to handle complex simulations that would be impossible with traditional infrastructure.
  • Explainable AI (XAI) and Human-Centered Explainable AI (HCXAI): 
    • Explainable AI (XAI) refers to methods for making AI model decisions understandable to humans, focusing on how the AI works, whereas Human-Centered Explainable AI (HCXAI) goes further by contextualizing those explanations to a user’s specific task and understanding needs. While XAI aims for technical transparency of the model, HCXAI emphasizes the human context, emphasizing user relevance, and the broader implications of explanations, including fairness, trust, and ethical considerations.
  • Physical AI & Embodied AI: 
    • Physical AI refers to a branch of artificial intelligence that enables machines to perceive, understand, and interact with the physical world by directly processing data from a variety of sensors and actuators.
    • Physical AI provides the overarching framework for creating autonomous systems that act intelligently in real-world settings. Embodied AI, as a subset, focuses on the sensory, decision-making, and interaction capabilities that enable these systems to function effectively in dynamic and unpredictable environments.
  • Federated Learning and Reinforcement Learning:
    • Federated Learning is a machine-learning technique where data stays where it is, and only the learned model updates are shared. “Training AI without sharing your data”.
    • Reinforcement Learning is a type of AI where an agent learns by interacting with an environment and receiving rewards or penalties. “Learning by trial and error”
    • Federated Learning (FL) and Reinforcement Learning (RL) can be combined into a field called Federated Reinforcement Learning (FRL), where multiple agents learn collaboratively without sharing their raw data. In this approach, each agent trains its own RL policy locally and shares model updates, like parameters or gradients, with a central server. The server aggregates these updates to create a more robust, global model. FRL is used in applications like optimizing resource management in communication networks and enhancing the performance of autonomous systems by learning from diverse, distributed experiences while protecting privacy.

 

 

• Fundamental Units of Electricity:
  • Ampere – Amp (A):
    • Amperes measure the flow of electrical current (charge) through a circuit. Ampere (A) is the unit of measure for the rate of electron flow, or current, in an electrical conductor.
      • One ampere is defined as one coulomb of electric charge moving past a point in one second. The ampere is named after the French physicist André-Marie Ampère, who made significant contributions to the study of electromagnetism.
      • Milliampere (mA) is a unit of electric current equal to one-thousandth of an ampere (1mA=10−3A). The prefix “milli” signifies 10−3 in the metric system. This unit is commonly used to measure small currents in electronic circuits and consumer devices.
    • Volts measure the force or potential difference that drives the flow of electrons through a circuit.
      • Kilovolt (kV) is a unit of potential difference equal to 1,000 volts.
    • Watts measure the rate of energy consumption or generation, also known as power.
  • Power vs. Energy: how electricity is measured and billed.
    • Power (measured in kW, MW, GW, TW): Rate at which energy is used or generated at a given moment.
    • Energy (measured in kWh, MWh, GWh, TWh): Total amount of power consumed or generated over a period of time (i.e., Power x Time).
  • Real Power Units: actual power that performs work.
    • Kilowatt (KW):
      • A kilowatt is simply a measure of how much power an electric appliance consumes—it’s 1,000 watts to be exact. You can quickly convert watts (W) to kilowatts (kW) by dividing your wattage by 1,000: 1,000W 1,000 = 1 kW.
    • Megawatt (MW):
      • One megawatt equals one million watts or 1,000 kilowatts, roughly enough electricity for the instantaneous demand of 750 homes at once.
    • Gigawatt (GW):
      • A gigawatt (GW) is a unit of power, and it is equal to one billion watts.
      • According to the Department of Energy, generating one GW of power takes over three million solar panels or 310 utility-scale wind turbines
    • Terawatt (TW):
      • One terawatt is equal to one trillion watts (1,000,000,000,000 watts). The main use of terawatts is found in the electric power industry, particularly for measuring very large-scale power generation or consumption.
      • According to the United States Energy Information Administration, America is one of the largest electricity consumers in the world, using about 4,146.2 terawatt-hours (TWh) of energy per year.
  • Apparent Power Units: measures the total power in a circuit, including power that does not perform useful work.
    • Kilovolt-Amperes (kVA):
      • Kilovolt-Amperes (kVA) stands for Kilo-volt-amperes, a term used for the rating of an electrical circuit. A kVA is a unit of apparent power, which is the product of the circuit’s maximum voltage and current rating.
      • The difference between real power (kW) and apparent power (kVA) is crucial. Real power (kW) is the actual power that performs work, while apparent power (kVA) is the total power delivered to a circuit, including the real power and the reactive power (kVAR) that doesn’t do useful work. The relationship between them is defined by the power factor. Since the power factor is typically less than 1, the kVA value will always be higher than the kW value.
    • Megavolt-Amperes (MVA):
      • Megavolt-Amperes (MVA) is a unit used to measure the apparent power in a circuit, primarily for very large electrical systems like power plants and substations. It’s a product of the voltage and current in a circuit.
      • 1 MVA is equivalent to 1,000 kVA, or 1,000,000 volt-amperes.
  • Specialized Power Units: used specifically for renewable energy, especially solar.
    • KiloWatt ‘peak’ (KWp):
      • kWp stands for kilowatt ‘peak’ power output of a system. It is most commonly applied to solar arrays. For example, a solar panel with a peak power of 3kWp which is working at its maximum capacity for one hour will produce 3kWh. kWp (kilowatt peak) is the total kw rating of the system, the theoretical ‘peak’ output of the system. e.g. If the system has 4 x 270 watt panels, then it is 4 x 0.27kWp = 1.08kWp.
      • The Wp of each panel will allow you to calculate the surface area needed to reach it. 1 kWp corresponds theoretically to 1,000 kWh per year.

 

 

• Liquid Cooling Components:
  • Direct-to-Chip (DTC) Cooling:
    • A Direct-to-Chip (DTC) cooling system is a liquid-cooling technology used to cool high-performance computer chips—such as CPUs, GPUs, and accelerators—by bringing a liquid coolant directly to the chip surface through a cold plate.
    • It is one of the most efficient cooling methods used in data centers, HPC clusters, and AI server farms.
  • Chillers:  
    • Mechanical systems that remove heat from a building’s liquid coolant, typically water, and transfer it to another location to cool the air and maintain comfort. Unlike traditional systems that might cool air directly, chillers generate chilled water that circulates through air handling units (AHUs) within the space to absorb heat, making them essential for cooling large commercial or industrial buildings.
  • Technology Cooling System (TCS): 
    • Broad, non-standard term that generally refers to any integrated system, including chillers, designed to manage heat in technological applications, like data centers or industrial processes, to prevent overheating
  • Condensors:
    • A condenser is a heat exchanger that cools a gas or vapor, causing it to condense into a liquid, a process that releases latent heat. These devices are critical in many systems, such as air conditioning, refrigeration, and power plants, where they use air or water to absorb heat from the vapor, transforming it back into a liquid state.
  • CDU (Coolant Distribution Unit): 
    • A coolant distribution unit contains a pump that circulates coolant through a network of pipes or channels, distributing it to various components like servers, processors, or other high heat components in large, high power devices that need cooling.
    • Coolant Distribution Units are essential in data centers with high-density applications providing close controlled coolant delivery and precise control of the liquid cooling system. They help manage heat loads, reduce power consumption, and increase efficiency and reliability through redundancy.
  • HDU (Heat Dissipation Unit):
    • Unlike a Coolant Distribution Unit (CDU) that rejects heat from the server rack to the chilled water loop of the building, a Heat Dissipation Unit (HDU™) rejects the server heat from the server rack to the white space where building air conditioning would then remove that heat.
  • RDHX (Rear Door Heat Exchanger) – Rack-level Heat Exchange: 
    • Chilled water cooling doors that fit onto a IT rack. The air is pulled through the rack by the fans or pushed through by the server fans for the passive rear door (no fans).

 

 

• Power Electronics:
  • Technology associated with the efficient conversion, control and conditioning of electric power by static means from its available input form into the desired electrical output form.
  • Branch of electrical engineering that deals with the processing of high voltages and currents to deliver power that supports a variety of needs.

 

 

• Tokens-Per-Watt (TPW):
  • Tokens-per-watt is a data-center efficiency metric for AI workloads. It measures how many AI tokens (text processed or generated by an LLM) a data center can produce per watt of power consumed. The higher the number, the more energy-efficient the AI compute is.

 

 

• Extra Low-Voltage (ELV):
  • Extra-Low Voltage (ELV) is defined as a voltage of 50V or less (AC RMS), or 120V or less (ripple-free DC).
• Low-Voltage (LV):
  • The International Electrotechnical Commission (IEC) defines Low Voltage (LV) for supply systems as voltage in the range 50–1000 V AC or 120–1500 V DC.
• Medium-Voltage (MV):
  • Medium Voltage (MV) is a voltage class that typically falls between low voltage and high voltage, with a common range being from 1 kV to 35 kV. In some contexts, this range can extend higher, up to 69 kV.
• High-Voltage (HV):
  • The International Electrotechnical Commission define high voltage as above 1000 V for alternating current, and at least 1500 V for direct current.
• Super High-Voltage or Extra High-Voltage (EHV): 
  • Super High-Voltage or Extra High-Voltage (EHV) is the voltage class used for long-distance bulk power transmission. The range for EHV systems is typically from 230 kV to 800 kV.
• Ultra High-Voltage (UHV): 
  • Ultra High-Voltage (UHV) is the highest voltage class used in electrical transmission, defined as a voltage of 1000 kV or greater.