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Overview: This article presents a review of control and modulation methods for DC-DC power converters. The focus is on high-performance power converters, but the methods are applicable to any DC-DC power converter. Pulse-width modulation (PWM) and small-signal-based feedback controls forms the basis of many commercial controller executions for DC-DC converters. Alternatively, many large-signal approaches are available. This article aims to provide a review of control and modulation methods, as well as methods for controller tuning, for DC-DC switching power converters.Do conventional control methods maximize efficiency?New, higher-level controls are inspired by the development of fast wide-bandgap switches in addition to the ongoing progress in digital signal processing and sensors. Fast processors and digital signal processing make new computational techniques for power converter control possible. Traditional methods of control almost never maximize available performance. The focus here is on high-performance converters, a rapidly expanding industry.Role of Converter TopologyThe converter topology serves as a constraint in the control process. In theory, with the right constraints, a single control method can be applied to a wide variety of circuits. Power regulation for digital electronics is very often done by voltage regulation. Using LED lighting encourages the use of current-regulated loads. Most battery chargers have settings for regulating both the voltage and the current. DC sources and loads in microgrids, as well as digital loads, benefit from droop relationships. The methods presented are not limited to these converter types; rather, they can be used with any DC-DC converter. Hard-switched converters, state feedback control, and large-signal tuning are all highlighted.Control Objectives for DC-DC ConvertersTable 1 summarizes the four different types of goals that DC-DC converter controls should meet. Both static and dynamic conditions are part of the operational necessities. Control is not always related to other operational needs, such as electromagnetic interference (EMI), efficiency, and dependability. The need for fault management and protection are typically dealt with independently. Some large-signal controllers can directly manage many requirements in Table 1 that appear to be independent. The entire set of specifications shown in Table 1 is related to converter design.Table 1. Converter Objectives With Control Implications. Source: IEEE Open Journal of Power Electronics Inductor and capacitor selection affect the ripple bands and slew rate limits. Layout and parasitics both have an impact on EMI. However, it is theoretically possible to define a cost function J(x) that is connected to all of the operating variables and converter parameters, as shown in equation (1)  where ai are weights, x are independent variables, and fi(x) are functions of x and other parameters. The root-mean-square (RMS) current and flux (associated with losses), the output voltage error and ripple, the rise time of the load current, the peak voltage stresses of the device, the peak junction temperature, and the switching frequency variation are examples. To take into account various operating points, converter topologies, and component considerations, the multi-objective optimization of power converters is formulated as a geometric program, a type of convex optimization problem. To increase the power density of DC-DC converters, it is also possible to incorporate electromagnetic effects and thermal management into the electrical design. Similar terms could have been used to define a performance index, which is the opposite of a cost function. An optimization problem can be formed from a design or control problem, and the cost function must be minimized.Control Methods to Address Timing ProblemThe timing issue is simple to frame but difficult to solve in practice. With simplified requirements, it is easy to solve for simple converters. However, the difficulty of the issue increases with the inclusion of further specification details and uncertainty. It does inspire particular methods. The goal of trajectory-based controls is to reformulate the timing problem as one with state variables. Alternatively, fast response relied on dedicated circuits like clamps. A converter is even modified with additional switches and devices to achieve faster disturbance rejection.Challenges with Solving the Timing ProblemBecause there is no simple solution to the generic switch timing problem, designers are limited to feasible methods. This typically adds two additional restrictions to those listed in Table 1. There are limitations on the converter's operating regime. Setting a mandatory minimum switching frequency is a typical example. The foundation for control design and operation is a simplified model of the converter. Implementing a small-signal linearization of an averaged model is a classic example. The first restriction reduces the amount of timing flexibility and makes the issue a cycle-by-cycle duty ratio. The second results in model-limited control, which may prevent access to the converter's full dynamic capabilities.Factors Affecting the Control MethodsThe block diagram of a fundamental feedback and feedforward buck converter control system is shown in Fig. 1. To prevent ripple effects, the feedback sensing block is band limited. Additional signal conditioning and analog-to-digital converters (ADCs) are required for digital control. For accurate output regulation or tracking, output feedback is necessary. Control or current-regulated loads can both benefit from inductor current feedback. Either output feedback or state feedback can be used to control a converter. Using input voltage, load current, or other data, feedforward action can improve disturbance rejection, lower audio susceptibility, and lower output impedance. To produce the gate signal for the controllable switch, the controller drives a modulator. A limiter function is necessary for the modulator in a boost converter. Fig. 1. Feedback control of a buck converter. Source: IEEE Open Journal of Power ElectronicsSmall-Signal ControlThere are a wide variety of uses for small-signal controllers. Network analyzers and other testing tools support the useful connection to conventional frequency-domain design procedures. Small-signal controllers have distinct soft start and inrush management, protection management, and strategies to adapt to a broad load range due to the need to design for a specified operating point. Improvements in dynamic performance are the subject of a large body of research. The advantage of connecting to well-established frequency-domain design tools is a benefit of small-signal models and tuning. However, small-signal methods and models do not offer a systematic way to run dynamic response up to slew rate limits and do not take into account nonlinear factors like duty ratio saturation or current limits. Also, small-signal controls require independent blocks for large-signal startup and fault protection.Large-Signal ControlLarge-signal controllers, on the other hand, can facilitate changes between seemingly incompatible operating states. Conversions can make use of the slew rate capabilities of the converter. Both switching boundaries and operating points can be applied to the problem of starting up and handling faults. Large-signal controllers provide useful alternatives for applications requiring fast dynamic response or a broad range of load conditions. Geometric controls can be visualized as involving multi-segment boundaries for functions like startup and fault protection. To conclude, the robustness and sensitivity issues between small-signal and large-signal methods are actually fairly consistent. Knowing the parameters is helpful for both; feedforward is advantageous for both; the model performs best when it is accurate and complete; and adaptation to changing circumstances is helpful for both.Summarizing the Key PointsNew, higher-level controls for power converters are possible due to the development of fast wide bandgap switches, digital signal processing, and sensors. Converter topology serves as a constraint in the control process, but with the right constraints, a single control method can be applied to a wide variety of circuits. Pulse-width modulation and small-signal based feedback controls are commonly used for converters, but large-signal approaches are also available. Geometric controls based on piecewise-linear large-signal analysis can provide the quickest dynamic response for high-performance DC-DC converters. Low-cost digital controls make it possible to sample quickly and switch boundary controls, and high-performance DC-DC converters may benefit from the use of online adaptive geometric controls.ReferenceKapat, Santanu, and Philip T. Krein “A Tutorial and Review Discussion of Modulation, Control, and Tuning of High-Performance DC-DC Converters Based on Small-Signal and Large-Signal Approaches.” IEEE Open Journal of Power Electronics 1 (2020): 339–71. https://doi.org/10.1109/ojpel.2020.3018311.

Overview: This article provides an overview of how artificial intelligence methods, including expert systems, fuzzy logic, metaheuristic techniques, and machine learning, can optimize power electronics systems. Artificial intelligence methods refer to a set of techniques and algorithms that enable machines to perform tasks that typically require human intelligence, such as learning, reasoning, perception, and decision-making. Artificial Intelligence MethodsAdvances in artificial intelligence are likely to yield substantial benefits for power electronics. Artificial intelligence methods can be broadly divided into expert systems, fuzzy logic, metaheuristic techniques, and machine learning.  Figure 1. Sankey diagram of artificial intelligence methods and applications in each phase of the life-cycle of power electronic systems. Image used courtesy of IEEE Transactions on Power ElectronicsExpert SystemThe oldest artificial intelligence technique that has been successfully used in industrial applications is the expert system. The expert system is essentially a database that incorporates the expert information into a catalog of Boolean logic that serves as the foundation for simulating the IF-THEN logic rules used by human brains to reason. An intelligent system that simulates the inference process using the database answers the why-and-how questions. The database comprises simulation data, facts, and claims or field expert knowledge. It can be updated continuously.  It is important to note that the utilization data in Figure 1 shows that expert system applications are as low as 0.9%. The expert system lacks universality since it is typically built on system principles and norms, which are closely related to the system of interest. It only applies to well-defined domains with reliable expert rules. Because of the quick growth of computer platforms, advanced artificial intelligence with improved inference and approximation skills, such as fuzzy logic and machine learning, can perform expert system functions. Fuzzy LogicFuzzy logic, which extends Boolean logic into multivalued conditions, is a rule-based approach similar to expert systems. To deal with system uncertainties and noisy measurements, fuzzy logic is the perfect instrument.  Fuzzification is first carried out using fuzzy sets made up of many membership functions with a range of 0-1 rather than using the exact input crisp value directly. The inference step then aggregates the fuzzy input signals using fuzzy rules. The inference result undergoes defuzzification by considering the level of fulfillment that produces a crisp value. To complete the nonlinear mapping between the input and output, the crisp value is modified in a fuzzy space using precisely constructed principles.  Components of Fuzzy LogicIn most applications, the four basic components of a fuzzy logic method are fuzzification, rule inference, knowledge base, and defuzzification. First, fuzzification is applied to the input of linguistic variables with membership functions such as triangular, trapezoidal, Gaussian, bell-shaped, singleton, and other custom-made shapes. Second, the inference module combines the signals following IF-THEN fuzzy rules drawn from expert experience and stored in the knowledge base. Third, defuzzification of the output signal is carried out. Metaheuristic MethodsOnce the optimization objective for a given application has been stated, the best solution can be found using either a deterministic programming method (such as linear or quadratic programming) or a nondeterministic programming method, such as the metaheuristic method. In deterministic programming techniques, their complexity in calculating the gradient and Hessian matrices makes them difficult to use in most optimization problems in power electronics.  For various optimization tasks, metaheuristic approaches act as a general end-to-end tool that requires less specialized knowledge and is effective and scalable. The development of metaheuristic algorithms frequently draws inspiration from biological evolution, as seen in the genetic algorithm (GA) that uses the natural selection process and the ant colony optimization (ACO) algorithm that mimics ants in looking for an effective food path. Trial-and-error is a method that promotes the search for the ideal response.  Metaheuristic Techniques and MethodsThe metaheuristic techniques fall into two categories: trajectory-based techniques (tabu search method, simulated annealing method, etc.) and population-based techniques (GA, particle swarm optimization (PSO), ACO, differential evolution, immunity algorithm (IA), etc.).  Trajectory-Based TechniquesFor the trajectory-based techniques, there is only one candidate solution included in each exploration step, and it develops into another solution by a set of rules. The standard and effectiveness of the rule largely determine the approach's effectiveness. As a result, for nonconvex optimization tasks, the ultimate solution is frequently a local rather than a global solution, and the convergence speed of trajectory-based approaches is typically slow.  Population-Based TechniqueThe population-based methods generate a large number of candidate solutions at random. To enhance the quality of the population in the current generation, these candidate solutions are either varied (e.g., crossover in the GA) or incorporated and replaced with fresh candidate solutions at each iterative exploration. As a result, the population's suitability is gradually increasing to get closer to the ideal solution. They are more effective than trajectory-based approaches regarding convergence speed and global searching ability and are particularly helpful for multiple optimization tasks.  However, population-based approaches have a heavier computing requirement. For online application scenarios where effectiveness and speed are crucial, this difficulty must be considered. A list of power electronics-related metaheuristic techniques, together with their benefits and drawbacks, is presented in Table I. In terms of several crucial characteristics, such as implementation ease, global convergence, convergence speed, and parallelism, these metaheuristic algorithms are qualitatively compared. Most optimization issues in power electronics are resolved using population-based approaches due to their significant advantages. Table 1 shows various population-based techniques with enhanced versions for power electronics optimization problems. They are created and enhanced using various biological influences.  Several other recently developed approaches, such as biogeography-based optimization, the crow search algorithm, grey wolf optimization, the firefly optimization algorithm, the bee algorithm, the colonial competitive algorithm, teaching-learning-based optimization, etc., have also been used on a limited scale in addition to the earlier, widely used metaheuristic methods.It is important to note that choosing the optimal strategy is a difficult task that depends on the application. As indicated in Figure 2, the two most common metaheuristic techniques used in power electronics are GA and PSO. They serve as the foundation and models, respectively, for evolutionary algorithms and swarm intelligence algorithms, upon which numerous variants are built. Practitioners can pick the method based on its superiority, as shown in Table I. Figure 2. Usage statistics of population-based metaheuristic methods in the optimization of power electronics Image used courtesy of IEEE Transactions on Power Electronics Machine Learning Machine learning is intended to automatically identify patterns and principles through experience gained from either data collection or interactions through trial and error. It is divided into three categories for use in power electronics: supervised learning, unsupervised learning, and reinforcement learning (RL). Summarizing the Key PointsArtificial intelligence methods have the potential to revolutionize power electronics by improving system efficiency, reliability, and performance.Expert systems can be used to diagnose faults in power electronics systems and provide recommendations for repair or replacement.Fuzzy logic can improve the accuracy of power electronics control systems by accounting for uncertainty and imprecision in sensor data.Metaheuristic techniques, such as genetic algorithms and particle swarm optimization, can be used to optimize power electronics systems by searching for the best combination of design parameters.Machine learning techniques, including supervised learning, unsupervised learning, and reinforcement learning, can automatically identify patterns and principles in power electronics data and improve system performance over time. ReferencesZhao, S., Blaabjerg, F., & Wang, H. (2021, April). An Overview of Artificial Intelligence Applications for Power Electronics. IEEE Transactions on Power Electronics, 36(4), 4633–4658. https://doi.org/10.1109/tpel.2020.3024914

Overview: This article explores the opportunities and challenges of integrating clean technologies and information and communication technologies for efficient and sustainable energy management in smart grids.  Decarbonization has accelerated the fundamental shift in society toward clean technologies. Electrical energy will be a significant factor in the decarbonization process. Electrical energy is one of the most common forms of energy carriers and is seeing growing usage. Increasing electricity demand forces the expansion of the generation and transmission systems, requiring a significant amount of investment.  Power loss and reactive power flow in the transmission systems make the conventional, centralized structure of power systems less efficient. Distributed generations (DGs) have been incorporated into low- and medium-voltage distribution networks in order to increase system availability, efficiency, and cost-effectiveness. Furthermore, renewable-based distributed generation aids in the decarbonization of the electric energy sector.Evolution of Smart GridDistribution systems that have been powered up can function as a microgrid in the absence of the utility grid. A microgrid is an island-based distribution system that uses local distributed generation and energy storage to provide critical loads in island mode. Distribution systems with microgrid capabilities will have some benefits, such as increased productivity, dependability, accessibility, and power quality.  However, information and communication technologies (ICTs) are necessary for the optimal and reliable operation of various distributed generation and energy storage systems in microgrids. To operate modern energy distribution systems as efficiently and dependably as possible, the smart grid concept has been introduced. To operate and plan grid systems with irregular output and variable power sources, ICTs must be available at both the generation and transmission levels. These systems enable power systems to meet customer demands by intelligently monitoring, making decisions, and controlling contemporary power systems. In addition to incorporating DGs into distribution networks, large-scale renewable power plants like photovoltaic (PV) and wind energy systems have been widely installed in power systems, and the power grids are currently moving toward more fully renewable energy systems. Figure 1. Concept of a Distributed Power Generation System Source IEEE Access Along with efficiency, flexibility, and operability benefits, smart grid technologies also present new difficulties for the design and management of modern power systems. Restructuring the power grids to incorporate renewable energy sources, microgrid technologies, ICTs, and power electronics can result in these difficulties. Smart Grid’s Future DirectionsThe idea of smart grids has changed with the development of technology. In recent years, the smart grid's research and development have increased. As a result, the implementation of smart grids has changed from virtual to real-time. However, there are several situations in which action needs to be taken to turn it into a complete real-time network service.Big Data ManagementThe input of real-time data is a key factor in a smart grid. It serves as the backbone of the network's functioning. Power transmission, generation, transformation, and utilization data are being collected for reliable and efficient working. All decisions are made based on the information gathered. The collection and management of such a vast amount of real-time data is a significant problem.  To predict the demand for energy at various locations, the algorithms must use all the data gathered from the sensors and associated devices. To produce the best results, the algorithms must be optimized. One of the main study subjects in smart grid technology is IT infrastructure, data gathering, governance, data processing, and, most critically, data security.Investing in Smart Grid InfrastructureTo reduce carbon emissions, a number of countries have started implementing smart grid infrastructure. Many of them are engaged in projects designed to evaluate the feasibility of the network. The construction of the smart grid infrastructure has already started in nations including Australia, South Korea, and Japan. The initial investment, though, is the main concern. The ongoing maintenance of the entire network further raises the overall cost.  Therefore, before making an investment of this size in the infrastructure, a thorough financial report should be made. The price of smart grids in a few emerging nations is shown in Table 1. This will estimate the starting sum that a developing nation must invest in order to create smart grid infrastructure. Additionally, it will provide a general concept of the maintenance costs as well as any other extra expenses necessary to guarantee the network's efficient operation.Business Model RestructuringThe business model has undergone considerable adjustment as a result of the new smart grid's emergence. New technologies have altered consumer perceptions and created a network of distributed power sources. Consequently, business practices are evolving. It is necessary to implement new policies to benefit consumer communications. To integrate the load and the generated power, the utility business model should be put into practice at the distribution level.Modernization of the Energy Production SystemCustomer needs have evolved due to the smart grid's evolution. As a result, there are fluctuations in energy demand. To accommodate the demand response, the system's capacity should be raised. Additionally, the energy-producing systems must change their production policies to integrate into the smart grid network. In the smart grid network, cloud-based data management strategies are applied. The existing system needs to be upgraded and changed in order to establish IoE activities.  Cyber-physical power systems are the smart operation of future power systems, which include distributed generation, microgrids, and demand side management while utilizing information and communication technologies over the physical system. The ICTs are vulnerable to cyberattacks, data loss, and hardware failure. ICT malfunctions will reduce system performance and must be taken into account when planning a power system. Additionally, when operating power systems, cybersecurity must be taken into consideration because malicious intrusions from cyberattacks could result in a loss of power or energy. The network should incorporate security measures against cyberattacks.Summarizing the Key PointsThe paper highlights the importance of information and communication technologies in the optimal and reliable operation of distributed generation and energy storage systems in microgrids.The integration of information and communication technologies with power systems can lead to the development of cyber-physical power systems or smart grids.Smart grids enable power systems to meet customer demands by intelligently monitoring, making decisions, and controlling contemporary power systems.However, the adoption of clean technologies and information and communication technologies presents new challenges for the design and management of modern power systems.Smart grid technologies also present new difficulties for design and management but offer significant benefits such as flexibility, efficiency, operability, reliability, accessibility, and power quality. Reference(s)1.Peyghami, S., Palensky, P., & Blaabjerg, F. (2020). An Overview on the Reliability of Modern Power Electronic Based Power Systems. IEEE Open Journal of Power Electronics, 1, 34–50. https://doi.org/10.1109/ojpel.2020.29739262.Pal, R., Chavhan, S., Gupta, D., Khanna, A., Padmanaban, S., Khan, B., & Rodrigues, J. J. P. C. (2021, August 28). A comprehensive review on IoT‐based infrastructure for smart grid applications. IET Renewable Power Generation, 15(16), 3761–3776. https://doi.org/10.1049/rpg2.122723.Rafique, Z., Khalid, H. M., & Muyeen, S. M. (2020). Communication Systems in Distributed Generation: A Bibliographical Review and Frameworks. IEEE Access, 8, 207226–207239. https://doi.org/10.1109/access.2020.3037196   

Overview: This article overviews communication technologies in smart grid infrastructure, focusing on electric vehicle charging protocols and standards. CatalogSmart Charging SystemCommunication Technologies in Smart Grid InfrastructureSummarizing with Key Points Smart Charging SystemTo develop a power distribution network that is both more effective and more environmentally friendly, the possibility of combining electric vehicles with smart grid technologies plays a significant role. A component of smart grids known as vehicle-to-grid (V2G) enables electric vehicles to not only receive power from the grid but also feed excess energy back into it when they have it available. The convergence of electric vehicles and smart grids has the potential to revolutionize the energy business while simultaneously lowering carbon emissions.Fig. 1 . Overall charging system for battery electric vehicles using wired/wireless charging technologies. Image used courtesy of IEEE Access Communication Technologies in Smart Grid InfrastructureEV charging protocols and standardsFig. 1 shows how the system for charging battery electric vehicles with wired and wireless charging works. The smart charging system connects with the entire system and gives the vehicles the best possible charge. A few common protocols are needed to establish proper communication between the entities. Tables 1 and 2 compare and identify some common communication protocols. Table 1: Wired communication technologies in the smart grid Source: IET Renewable Power Generation FamilyStandardData RateCoveragePLCNB-PLC: ISO/IEC 14908–3 (Lon- Works) ISO/IEC 14543–3-5 (KNX), CEA-600.31 (CEBus) BB-PLC: TIA-1113 (Home Plug 1.0), IEEE 1901, ITU-T G.hn (G.9960/ G.9961)NB-PLC: 1–10 Kbps for low data rate, 10–500 Kbps for high data-rate  BB-PLC: 1–10 Mbps (up to 200 Mbps on very short distances)NB-PLC: 150 km or more    BB-PLC: 1.5 kmOptical FibreIEEE 802.3ah ITU-T G.983 (BPON) IEEE 802.3ah (EPON)100 Mbps 155,–622 Mbps 1 Gbpsup to 10 km up to 20–60 km 10–20 kmDSLITU G.992.1 (ADSL) ITU G.992.5 (ADSL2+) ITU G.993.1 (VDSL)1.3–Mbps 3.3–24 Mbps 52–85 MbpsUp to 4 km Up to 7 km Up to 1.2 km Table 2: Wireless communication technologies in the smart grid Source: IET Renewable Power Generation FamilyStandardData RateCoverageWi-FiIEEE 802.11e (QoS enhancements) IEEE 802.11n (ultra-high network throughput)BIEEE 802.11s (mesh networking) IIEEE 802.11p (WAVE: wireless access in vehicular environments) Up to 54 Mbps  Up to 600 Mbps 300 m (outdoors)  Up to 1 kmWiMaxIEEE 802.16 (fixed and mobile broadband wireless access)IEEE 802.16 m (advanced air interface)128 Mbps down and 28 Mbps up 100 Mbps for mobile users, 1 Gbps for fixed usersUp to 10 km 0–5 (optimum), 5–30 (acceptable), 30–100 (reduced) km3G / 4GI3G: UMTS (HSPA, HSPA+)   4G: LTE, LTE-AdvancedHSPA: 14.4 Mbps down and 5.75 Mbps up HSPA+: 84 Mbps down and 22 Mbps upLTE: 326 Mbps down and 86 Mbps up LTE-Advanced: 1 Gbps down and 500 Mbps up0–5 km   LTE-Advanced: 0–5 (optimum), 5–30 (acceptable), 30–100 (reduced) kmSatelliteLEO: Iridium, Global Star  MEO: New ICO  GEO: Inmarsat, BGAN, Swift, MPDS2.4 to 28 Kbps  9.6 up to 128 Kbps  384 up to 450 KbpsDepend on the number of satellites and their beams.Depend on the number of satellites and their beams.Depend on the number of satellites and their beams.Open Charge Point Protocol (OCPP) This application-based protocol implements the communication infrastructure between the charging station and the centrally distributed management system. The application protocol is freely accessible. A vendor-oriented protocol was created by the Open Charge Alliance. Due to the quick access to information that electric vehicle drivers provide, it offers more versatility.  The primary characteristics that this particular system is equipped with include transaction management, security, smart charging, message display, and the generation of warnings in the event of a malfunction. A bidirectional international communication standard is ISO 15118. It is employed as a channel of information exchange between electric vehicles and the infrastructure. Additionally, it is utilized for vehicle-to-grid mode communication.  It needs a standardized platform that can deliver and manage the protocol and its services to implement the protocol. The Driivz platform, an open charge point protocol, is one such platform. It supports the OCPI, OCHP, open intercharge protocol (OICP), and open automated DR protocol (OADR). The Driivz platform also supports ISO 15118 and OCPP 2.0, enabling vehicle-to-grid communication technologies.Open Charge Point Interface (OCPI) This system was implemented to allow charging station operators and the electric mobility service to exchange information about charging points. The following is a list of the open charge point interface's characteristics: The location status and session information are both being updated.Remote command sending.Giving charge information records to give the correct billing amount.Authorizing charging stations through the token exchange.OADRIt is intended for information exchange among the systems to study the DR. To precisely estimate demand at peak periods when it is in operation; it is standardized to send and receive accurate information between distributed energy resources and the control system of the energy management system. It predicts demand accurately at peak times during its operation.Open Smart Charging ProtocolThis protocol enables communication between an energy management system and a charge point management system for a site owner. It can share immediate predictions on the local energy grid's ability to support a charge point operation.OICPHubject was the one who developed it. It is used for standardized communication between charge point operators and e-mobility service provider systems.Global System for Mobile (GSM)It is the most widely used mobile network today. It runs in the range of 900 and 1800 MHz and is based on circuit switching. With a data rate of up to 270 kbps, the modulation method known as Gaussian Minimum Shifting Key is employed. The mobile handset, base station sub-system, networking switching substation, and operation support substation are the four major subcategories of this protocol's architecture. One of the most secure communication system protocols to date is thought to be this one.General Packet Radio ServiceThis is a packet-based data transfer protocol. Compared to the GSM, this network enables IP-based applications to operate at substantially higher data transfer rates. This specific networking protocol is mostly used for smart grid applications in remote regions.Summarizing with Key PointsEffective communication technologies are essential for successfully implementing smart grid infrastructure, particularly in the context of electric vehicle charging protocols and standards.The open charge point protocol is a widely used application-based protocol that enables communication between charging stations and centrally distributed management systems.The open charge point protocol offers versatility and quick information exchange between electric vehicle drivers and infrastructure, with features such as transaction management, security, smart charging, message display, and warning generation.In addition to the open charge point protocol, there are other common communication protocols used in smart charging systems that facilitate proper communication between entities involved in the charging process.Overall, effective communication technologies play a crucial role in ensuring efficient and reliable electric vehicle charging infrastructure within smart grid systems. This blog post is part of a full research article from the IET Renewable Power Generation. The featured image is courtesy of Midjourney.

Overview: This article explores how the smart grid, with its IoT-based transactions, can help address energy challenges in the 21st century. Learn about the role of renewable power generation and electrical grid infrastructure in energy conservation.  "Smart Grid" (SG) refers to the upgraded electrical grid that was made possible by advances in communication and sensor technology. Developing smart grid infrastructure is one of the solutions to many problems regarding energy conservation.Challenges in Energy TransactionsThere is an increase in the amount of energy produced by solar and wind sources. Additionally, there are new loads, such as electric vehicles, heat pumps, smart residential cities, commercial and industrial usage, infrastructure, substations, etc. Due to these characteristics, additional technological challenges, notably the unpredictability of solar, wind, and electric vehicle charging stations, represent a significant challenge in the process of distributing energy, which is a critical issue.  Energy demand has been rising rapidly due to the expansion of industries and population density. To prevent an energy crisis in the future, attention is being paid to energy consumption. Due to a lack of dependability, efficiency, security, seamless connectivity, etc., conventional electrical energy and networks would not be able to meet the needs of the industry in the 21st century. As a result, many new technologies (including communication and sensors) have developed to offer the features listed above.Evolution of Internet of EnergyThe Internet of Things (IoT) has evolved due to the expansion of heterogeneous networks and smart devices, enabling all networks and devices to interact with one another and create communication links with one another. The Internet of Things will be very helpful in the smart grid because it manages numerous components and seeks to give users the best possible energy.  The Internet of Things (IoT) is becoming more popular in smart grids under the "Internet of Energy" (IoE). To deliver the best energy and share relevant data among the numerous entities connected to the grid, smart grid technology uses all newly developed communication technologies and creates a completely connected network. A major problem has been the administration of enormous amounts of real-time data and its integration.  In contrast to the Internet of Things, the Internet of Energy is one of the most recent approaches to addressing issues like uninterruptible services, optimal use, etc. This article describes how the smart grid will use the Internet of Things to manage energy effectively. This also discusses how communication technologies integrate various smart grid components, infrastructure entities, substations, electric vehicles, etc. Advantages of Internet of Energy-based Smart GridsThe Internet of Energy enables optimal power distribution to all grid-connected devices and information sharing inside the grid network. Energy management, electric vehicle integration, and network integration will all be crucial in smart grids. Vehicle-to-grid (V2G) and grid-to-vehicle (G2V) technology have established a road to deal with the integration. With this technology, automobiles can communicate data with infrastructure about their state of charge (SOC), battery life, and condition, in addition to receiving the best possible energy supply. Due to the rapidly expanding energy consciousness, a dependable system that can deliver high-quality energy with optimal output and a sustainable backup system is required. This is why the smart grid is so unique because of the way it became linked to the bidirectional network system. The multi-agent system (MAS) will be employed in industries to manage the smart grid without human interaction. The software component known as the multi-agent system is responsible for gathering and delivering necessary data throughout the network. Challenges in SecurityThe effective formation of communication between entities aids in the handling of the massive amount of real-time data using reliable, secure encryption techniques. Only permitted entities should be able to manage network data exchange. Data management and security will become important challenges while dealing with a large volume of data and powering every device connected to the grid. The grid network will be more vulnerable to cyberattacks, which might cause individual components and the network as a whole to malfunction.  It results in the flow of incorrect information between entities and end users. Therefore, it is necessary to give the grid high security. Strong protocols (including encryption and decryption), anti-malware software, and highly secure network management protocols are required for high security.Features of Internet of EnergyThe smart energy infrastructure shown in Fig. 1 is a networked system comprising loads, energy metering units, energy storage devices, and automated and centralized distribution systems. Power and energy distribution across the network is the Internet of Energy’s primary goal, and it also enables information sharing with all linked devices. It deals with the security and management of real-time data. Cloud and edge-based systems are fully necessary for implementing the "Internet of Energy" concept.  Open-source interfaces are necessary for creating customer-specific applications to make the Internet of Energy quick and effective. The cloud-based application system at the power grid substation compares the actual target with the current target demand. It offers services like security management and power delivery to remote locations. The substation-connected assets were tracked, examined, and shared using the Internet of Energy.  Once the data analysis process is complete, the appropriate entity will permit the necessary steps, transforming the power plant and smart grid from a traditional into a virtual system. The advanced distributed energy management system's use of technology improves the effectiveness of power usage. Utilizing appropriate optimization techniques at various levels maximizes output while lowering costs, boosting profitability, improving dependability, and incorporating more renewable resources into the smart grid network.  The administration of smart meters, grid analytics, sub-station devices, low voltage outage management systems, and distributed energy resource management systems are some advanced applications integrated with the Internet of Energy. By integrating real-time data and devices into the digital world, smart grids offer quick and safe transport of information and power. Fig. 1. Internet of Things-based efficient energy transactions at the grid and charging stations. Source: IET Renewable Power Generation Summarizing the Key PointsThe use of Internet of Things-based efficient energy transactions is crucial in addressing the challenges faced by conventional electrical energy and networks in meeting the demands of the industry in the 21st century.The Internet of Things has played a significant role in the evolution of the electrical grid, enabling all networks and devices to interact with one another and create communication links.The Internet of Things-based efficient energy transaction can help prevent an energy crisis in the future by ensuring that energy demand is met efficiently and securely.The Internet of Energy is becoming more popular in smart grids as it seeks to give users the best possible energy by managing numerous components and providing uninterruptible services.The administration of enormous amounts of real-time data and its integration has been a major problem in smart grids, which can be addressed using Internet of Things-based efficient energy transactions. This blog post is part of a full research article from IET Renewable Power Generation. The featured image is courtesy of Midjourney. 

Electrical engineering and electronics traverse many fields of technological innovations and are in the foreground for groundbreaking advancements. Over the years, electrical and computer engineering have pioneered and contributed to developing more streamlined design, development, testing and improved manufacturing processes for frontier-end electronics, devices and equipment. In the strive for streamlining and in pursuit of innovation, the electrical and electronics industry has rapidly edged into the ever-expanding applications of artificial intelligence and machine learning.With the emergence of new technologies such as IoT, artificial intelligence (AI), machine learning (ML) and deep learning, the electronics and electrical industry is adopting and embracing major changes that lead to faster workflows, through optimization, automation and removing redundancies. ML and AI in industrial setups are designed to optimize systems and improve efficiency. This is possible as these systems are equipped with sensors and analytical processes that compute and interpret the data providing useful information.For instance, engineers create networks of interconnected cameras and sensors to guarantee that an autonomous vehicle's AI can "see" its surroundings. They must also make sure that the data is transmitted from these onboard sensors as quickly as possible because any lag in processing might cause a serious mishap.The electrical and electronic industry has seen significant advancements in recent years, with the emergence of artificial intelligence (AI) and machine learning (ML) revolutionizing the way we design, build, and operate electrical systems and devices. AI and ML technologies are increasingly being used to optimize performance, reduce costs, and improve efficiency across a range of applications in the industry. From predictive maintenance and fault detection to energy management and personalization, AI and ML are transforming the electrical and electronic industry in unprecedented ways. This article provides an overview of the role of AI and ML in the industry, examining their applications, benefits, and prospects.To start with let us understand what the terms AI and ML mean and how they are different.What are AI and ML?Artificial intelligence (AI) and Machine learning are often used synonymously although there are some differences between these two terms. To understand AI, it is important to first define what is machine learning and differentiate it from artificial intelligence.Artificial Intelligence is commonly known as AI, which refers to the development of computer systems, embedded systems and logical processing that can perform tasks that typically require human intelligence, such as learning, problem-solving, decision-making, and language processing.  For computer systems to learn and mimic human intelligence, they need to learn from a large dataset. For computers to learn from data, adapt to changing settings, and carry out activities that would otherwise require human involvement, artificial intelligence (AI) entails the creation of algorithms, machine learning models, and other approaches. Self-driving cars, voice recognition, image and speech recognition, fraud detection, and many more uses for AI are becoming more prevalent.Machine Learning or ML for short is a basic subset of AI. Basically, it involves the design and development of simple or complex algorithms and models that enable computers to learn from data, make analyses and improve their performance on a task without being explicitly programmed. Machine learning algorithms can learn from a large dataset to identify patterns, correlations and relationships, and then use that knowledge to make predictions or decisions on predictive new data. Machine learning can thus be described as a way of predicting the future based on presented parameters. There are three main types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. ML is used in a wide range of applications, including image and speech recognition, natural language processing, recommendation systems, and predictive maintenance, among others.Artificial Intelligence and Machine Learning in the Electrical and Electronics IndustryThe term artificial intelligence as used in the electrical and electronics industry generally refers to a variety of systems and technologies built to imitate human intelligence by making decisions and solving related problems. In recent years, engineers and scientists have explored different applications and ways in which AI can be applied in electrical systems.Some of the most common ways in which AI is incorporated into electrical systems and consumer electronics:• Expert systems: Expert systems are a subset of artificial intelligence (AI) systems that employ an inference engine to derive conclusions from a knowledge base containing data on a particular area. If-then rules that are drawn from the knowledge of human subject-matter experts often make up the knowledge base. To offer a solution or suggestion, the inference engine applies these principles to the data or issue at hand. Since the 1970s, expert systems have been utilized for a variety of tasks, including financial planning, technical problem-solving, and medical diagnosis. They have the potential to successfully automate decision-making procedures and lessen the requirement for human expertise in specific jobs. They may not always deliver correct or timely information due to their limits in dealing with complicated or dynamic circumstances.• Fuzzy logic control: Fuzzy logic is a mathematical framework that deals with uncertainty and imprecision, and fuzzy logic control systems employ fuzzy logic to make choices and manage operations. Traditional control systems base judgments on exact numerical values, whereas fuzzy logic control systems base decisions on the degree to which linguistic variables are members of fuzzy sets. In complicated control systems, where it may be challenging to set precise numerical values for every input variable, fuzzy logic control systems are particularly helpful in cases when there is ambiguity or imprecision in the data.• Machine Learning: The creation of algorithms and models for machine learning (ML) enables computers to learn from data and enhance their performance on a job without being explicitly programmed. A huge dataset may be used by machine learning algorithms to detect patterns and correlations, which can subsequently be applied to fresh data to produce predictions or choices.• Artificial Neural Networks: A class of machine learning model known as artificial neural networks (ANNs) is modelled after the structure and operation of biological neural networks in the human brain. Artificial neurons, also known as neurons, are linked nodes that are arranged in layers to form ANNs. Each neuron in the network takes input signals from neighbouring neurons or outside sources, analyzes them using an activation function, and then generates an output signal that is sent to other neurons.• Deep Learning: To train artificial neural networks with many layers and enable them to learn hierarchical representations of the input data, deep learning is a subset of machine learning. Deep learning models are highly suited for a variety of applications, including image and audio recognition, natural language processing, and autonomous systems since they can automatically learn to discover complex patterns and correlations in the data. To alter the weights of the connections between the neurons in the network, deep learning models are often trained using a large dataset and an optimization technique, such as stochastic gradient descent.Application of AI and ML in the Electrical and Electronics IndustryThe design, construction, and use of electrical systems and devices are being transformed by artificial intelligence (AI) and machine learning (ML), which are becoming more and more relevant in the electrical and electronic industries. The industry is being impacted by AI and ML in the following ways:• Predictive maintenance: AI and ML algorithms may be used to identify when electrical equipment is most likely to fail and plan repair, cutting down on both maintenance costs and downtime.• Optimization: AI and ML models can be used to analyze large sets of data and make the decision based on the data. Machine learning algorithms can be used to optimize the performance of electrical systems and devices, ensuring that they are operating at peak efficiency. This optimizes processes, equipment and devices to perform more efficiently.• Fault detection and diagnosis: AI and ML algorithms can be used to detect and diagnose faults in electrical systems, allowing for more accurate and efficient troubleshooting and repair. The process can be based on the information collected by the sensor in systems or by predictive analysis based on previous parameters.• Energy management: Energy management is one of the most vastly used applications of AI due to its ability to compute and make a logical decision in homes and industrial setups. AI and ML can be used to optimize energy consumption in buildings and homes, reducing energy waste and saving money on energy bills.• Design optimization: AI and ML algorithms can be used to optimize the design of electrical systems and devices, improving performance and reducing costs.• Robotics and Automated Assembly: By automating the assembly of electrical components, AI and ML can increase productivity and decrease the need for manual labour. Algorithms can quickly and accurately detect and manipulate electrical components using computer vision and other approaches AI and ML are critical for developing and improving the performance of robots and automated systems used in manufacturing and other industries.• Personalization: Smart bulbs, smart homes and smart devices are based on the idea of customizing and personalizing technology. AI and ML can be used to create personalized electrical devices that adapt to individual user preferences, improving the user experience. These devices operate depending on the preferences of an individual or a particular setting thus making them customizable. • Smart Grid Management: The electricity grid can be managed more effectively with the help of AI and ML. Algorithms can find possibilities to decrease energy waste and suggest modifications to optimize the distribution of electricity by evaluating data on energy consumption trends.• Quality Control: Quality control in the traditional manufacturing process and assembly is a manual process that is often inaccurate and done through sampling. Throughout the manufacturing process, electrical component flaws may be automatically detected using AI and ML. Algorithms may find flaws and eliminate problematic parts by evaluating photos and other data, therefore raising the calibre of the final output.Overall, artificial intelligence (AI) and machine learning (ML) are revolutionizing the electrical and electronic sectors, allowing engineers to create, construct, and use electrical systems and gadgets more successfully and efficiently than ever before.FAQs1.What is the difference between AI and machine learning? AI refers to the broader field of creating intelligent machines that can perform tasks that typically require human intelligence, while machine learning is a subset of AI that involves training algorithms to make predictions or decisions based on data.2.How are AI and ML used in the electrical and electronics industry? AI and ML are used in a wide range of applications in the electrical and electronics industry, including predictive maintenance, process optimization, fault detection and diagnosis, energy management, and smart grid management.3.What are the benefits of using AI and ML in the electrical and electronics industry?The benefits of using AI and ML in the electrical and electronics industry include improved efficiency, increased productivity, reduced downtime, improved safety, and cost savings.4.What are the challenges of implementing AI and ML in the electrical and electronics industry? The challenges of implementing AI and ML in the electrical and electronics industry include the need for large amounts of high-quality data, the complexity of developing and training models, the cost of implementing new technologies, and the need for specialized skills and expertise.5.What are some popular AI and ML techniques used in the electrical and electronics industry? Some popular AI and ML techniques used in the electrical and electronics industry include artificial neural networks, fuzzy logic control systems, deep learning, reinforcement learning, and genetic algorithms.6.What are some examples of AI and ML applications in the electrical and electronics industry? Some examples of AI and ML applications in the electrical and electronics industry include energy demand forecasting, predictive maintenance of electrical equipment, automated fault detection and diagnosis, and optimization of power grids.