Industrial Smart Grid Research Articles

Shunt Active Power Filter for Power Quality Improvement of Renewable Energy Systems: A Case Study

This paper introduces an application of an active power filter (APF) in a real industrial zone smart grid for power quality (PQ) improvement issues. The random harmonics generated by on-grid PV inverters and non-linear loads that represent the topology of the industrial smart grid are mitigated, also the reactive power, voltage levels, and power factor were adjusted using a shunt active power filter (SAPF). Detailed design of APF and its hysterics control strategy were presented using the MATLAB/SIMULINK software package. The results prove that SAPF is an effective device to mitigate total harmonic distortion (THD), and has a fast dynamic response to regulate the grid's power factor (PF).

Power Quality Improvement using Shunt Active Power Filter: An Industrial Zone Case Study

Power quality (PQ) improvement using active power filters (APF) is a topic of growing interest. APFs are effective in reducing harmonic distortion and improving power factor. They can be used in a variety of applications to improve the performance and reliability of electrical equipment. To address concerns with PQ improvement, this study offers an application of shunt APF in an industrial zone smart grid. the non-linear loads and unpredictable harmonics produced by on-grid PV inverters that represent the architecture of an industrial smart grid. Utilizing the MATLAB/SIMULINK software suite, a detailed design of the APF and associated hysteresis current control technique is provided. The findings demonstrate that APF is a useful tool for reducing total harmonic distortion (THD) and has a quick dynamic reaction to control grid voltage and power factor.

ДОСЛІДЖЕННЯ ЕФЕКТИВНОСТІ ВИКОРИСТАННЯ РОЗПОДІЛЕНОЇ МАТРИЧНОЇ ФАКТОРИЗАЦІЇ В ПРОМИСЛОВИХ СИСТЕМАХ

The processing of big data is an exceedingly urgent challenge in the functioning of modern information systems. The latest information technologies must be employed to collect, store, and analyze vast amounts of information. Intelligent data processing systems were implemented in numerous fields, particularly in the industry. Smart industrial systems also utilize data from various devices, enabling automated management processes and network component analysis. A prime example of an intelligent industrial system is the smart grid, which efficiently distributes electricity to users by considering demand, network parameters, load, etc. Processing large amounts of information necessitates the use of machine learning methods and mathematical data analysis. Matrix factorization serves as an exemplary technique for transforming information into a more convenient form for further processing, establishing relationships between elements, and optimizing outcomes. In particular, the SVD (Singular Value Decomposition) and Funk-SVD algorithms are employed to address big data processing challenges, and they were discussed in this work. The key features of processing large data volumes in industrial smart grid systems were analyzed in the paper. The advantages of distributed computing for more efficient information analysis were identified. The recommendation algorithms that enable faster and more accurate processing of extensive data were explored in the study. Specifically, the SVD and Funk-SVD algorithms, used in recommendation systems for large data processing, were examined. A method of distributed matrix factorization to provide recommendations to smart grid system users was proposed in the paper. This approach involves the exchange of public data between devices and the local processing of private data. The advantages of this distributed model include flexibility in adjusting parameters, improved calculation accuracy through result exchange between nodes, high data processing speed, and scalability were identified. The conclusion that the proposed method can be effectively used in recommendation systems within the smart grid context, enhancing automated management processes and resource distribution was exclaimed.

Control over Grid Reactive Power by Using a Powerful Regenerative Controlled-Speed Synchronous Motor Drive

The authors propose a technique for reactive power compensation using a powerful regenerative controlled-speed synchronous motor drive (SMD) based on a three-level (3L) neutral point clamped (NPC) active front-end rectifier (AFE) and a voltage source inverter (VSI). The review of technical solutions for reactive power compensation showed that the limitations on the transmitted reactive power in the system under consideration still have not been studied. The paper provides a mathematical description and proposes synthesis-friendly block diagrams of the mathematical 3L-NPC-AFE-VSI and SMD models. The developed models allow defining the instantaneous values of the total 3L-NPC-AFE power consumed from the grid depending on the SMD load diagram. It is noted that the 3L-NPC-AFE-VSI-SMD system is designed without considering the opportunities for reactive power generation. It was determined that the limit value of reactive power generated by a 3L-NPC-AFE depends on the DC link voltage, the grid current consumption and the modulation index. The possibility of reactive power compensation by the SMD system through a 3L-NPC-AFE was experimentally tested on the main drive of a metal plate hot rolling mill. The analysis of the results obtained showed that during the breakdown, an SMD can generate reactive power equal to 16% of the total rated power using a 3L-NPC-AFE at a rated DC link voltage and without overcurrent. It was shown that generating reactive power is expedient in low-load SMD operation modes or at idle. Research in this area is promising due to the widespread use of high-power SMD based on a 3L-NPC-AFE-VSI and the tightening of requirements for energy saving and efficiency and supply voltage quality. The proposed reactive power control technique can be used as part of an industrial smart grid.

Automatic Fault Detection in Industrial Smart Grids Using KNN and Ensemble Classifiers

The use of sensitive electrical gadgets in industries, buildings, smart cities, and homes has increased drastically in recent years. PQ events such as interruptions, surges, and sags have a high impact on these sensitive devices. The failure of these delicate devices in real-time applications, particularly smart applications, may result in significant damage. The supply quality decreases because of the failure of internal transmission system elements, unbalanced loads, and other outdoor issues such as like weather. Several academics have proposed techniques to analyze these PQ disturbances, including wavelet packets, S-transform, rough sets and neural networks. In all the available algorithms, the classification procedure involves the extraction of a large set of features from the transformed outputs, training the classifier, and finally making a conclusion with the classifier. Because of the involvement of a large number of features, the computational cost of all these methods increases. To reduce complexity and enhance classification efficiency, the proposed method focuses on extracting fewer low-complexity wavelet features from signals. Pattern recognition (PR) methods, such as the wide variety of K-nearest neighbors (KNN) and ensemble classifiers, are used to classify PQ events in this study. The performance of the proposed ML approaches' performance is evaluated at various training and testing rates. Subsequently, the performance of the proposed strategies was compared to that of the current methods to determine the dominance of the proposed approaches.

Energy flexible heat pumps in industrial energy systems: A review

This paper aims for a review of the operational and design approaches of industrial heat pump systems within industrial smart grids. This work identifies the strengths and potentials of energy flexible heat pumps integrated into the industrial energy system. Several review papers have already worked on the topic of energy flexible heat pumps in smart grids, usually addressing household energy systems. Therefore, a review of the academic literature is carried out, aiming to identify technical operation and integration approaches of energy flexible heat pumps in industrial energy systems. Furthermore, the differences between energy flexible heat pumps in small-scale systems and in large-scale energy systems are pointed out.

Privacy-Preserved Generative Network for Trustworthy Anomaly Detection in Smart Grids: A Federated Semisupervised Approach

The deep integration of industrial internet of things technologies in the industrial smart grid (ISG) brings many privacy and security attacks, threatening the trustworthiness of underlying system infrastructures and associated services. That, in turn, raises the necessity for anomaly detection staged by appropriate authorities. Deep learning can provide a promising solution for anomaly detection, but it remains untrustworthy as it fails to do well with small-size labeled data and class-imbalanced data. To solve these issues, this article introduces a novel privacy-preserving federated semisupervised class-rebalanced (Fed-SCR) framework for the detection of anomalous power data in fog-assisted smart grids. Fed-SCR introduces a semisupervised generative network to enhance the quality of generated minority samples and model the relationships between labeled and unlabeled data. Moreover, the generator and discriminator are block-structured leveraging temporal convolutions to improve the representation power during the training. A novel aggregation scheme [termed federated geometric median aggregation (Fed-GMA)] is also introduced based on selective and periodic geometric-median-based aggregation with the main aim to increase the robustness of the federated semi supervised class-rebalanced (Fed-SRC) against noisy gradients while maintaining communication efficiency. The evaluations of Fed-SCR on public power grid datasets reveal its efficiency in improving the trustworthiness of the ISG platform, outperforming the competing methods in terms of binary classification (accuracy: 97.28) and multiclass classification (accuracy: 96.36–95.04).

Generative Edge Intelligence for Securing IoT-assisted Smart Grid against Cyber-Threats

The critical dependence of industrial smart grid systems on cutting-edge Internet of Things (IoT) technologies has made these systems more susceptible to a diverse array of assaults. This consequently puts at risk the integrity of energy data as well as the safety of energy management activities that depend on those data. This study offers a generative federated learning framework for semi-supervised threat detection in an IoT-assisted smart grid system. We refer to this framework as FSEI-Net. A unique semi-supervised edge intelligence network (SEI-Net) is presented in the FSEI-Net to enable semi-supervised training using labeled and unlabeled data in the edge tier. The design of SEI-Net is based on with bidirectional generative convolutional network that can intelligently capture the patterns of threat data from partially labeled smart grid data. We present federated training to enable remote edge servers to work together on training a semi-supervised detector without disclosing their own private local data. This is accomplished through cooperative training. To facilitate communication between cloud and edge layers that is both secure and respectful of users' privacy, a reputation-based block chain is introduced in the FSEI-Net. The outcomes from the practical applications demonstrate that the effectiveness of the proposed FSEI-Net over the most recent cutting-edge detection approaches are valid

ISG-SLAS: Secure and lightweight authentication and key agreement scheme for industrial smart grid using fuzzy extractor

Smart grid (SG) has been received significant attention due to various services such as renewable energy and demand response. However, SG is fragile to various security attacks because an adversary can eavesdrop, insert, delete, or intercept the exchanged messages over an open channel. Thus, secure and lightweight authentication and key agreement (AKA) scheme for SG to ensure the necessary security requirements. Recently, the existing schemes designed a secure and efficient AKA protocol for industrial SG using cryptographic primitives to ensure reliable energy services. However, we prove that existing schemes for industrial SG are vulnerable to potential security attacks and do not guarantee the necessary security requirements. To enhance the security flaws of the existing schemes, we design a secure and lightweight AKA scheme for SG using fuzzy extractor, called ISG-SLAS. ISG-SLAS resists various security attacks and provides security functionalities. We evaluate the security of the ISG-SLAS using ROR model and AVISPA simulation. Moreover, we present the testbed experiments using MIRACL based on Raspberry PI 4. We then present a comparative analysis between ISG-SLAS and related schemes. Consequently, ISG-SLAS better ensures security and efficiency compared with related schemes and is more suitable than related schemes for practical SG.

Optimal integration of demand response programs and electric vehicles in coordinated energy management of industrial virtual power plants

Today, implementation of an efficient and economical energy management system (EMS) in industrial smart grids- given the rising trend in electrical load consumption and restrictions in ramping up the power plants' capacities- cannot be underestimated. Optimal planning avails itself of the exploitation manner of existing energy resources alongside appropriate employment of demand response (DR) programs and available electric vehicles (EV) in parking lots of immense industrial centers as storage sites in energy management systems in industrial smart grids have been seriously considered. This article introduces a new perspective in the simultaneous use of demand response loads and available electric vehicles in parking lots toward energy management of industrial virtual power plants (IVPPs) aiming at augmenting the system's profits as well as increasing the grid reliability under peak load conditions and cutting down on industrial centers load shedding. The objective function of the problem is formulated within the framework of short-term production planning for distributed energy resources (DER) and conventional ones in conjunction with DR programs and EV in such a way that the IVPPs' profits are maximized. In order to validate the performance of the proposed method, we have applied and tried on the second zone of IEEE-RTS standard grid. The outcomes accrued from various simulations point to the fact that the presence of EV within the boundaries of EV's parking lots prompts considerable increase in IVPPs' storage amount and consequent decline in exploiting full power grid capacity. Utilizing DR programs for responding load and selecting best scheduling schemes for each IVPP during various times of a day-night cycles will bring about a reduction in operation costs, de-peaking as well as an enhanced performance of EV's parking installations.

Secure Metering Data Aggregation With Batch Verification in Industrial Smart Grid

Smart grid can greatly improve the efficiency, reliability, and sustainability of the traditional grids. In industrial smart grid, real-time user-side metering data may be frequently collected for monitoring and controlling electricity consumption. However, the procedure of frequently metering data collection may lead to sensitive information leakage. To address the security issues in industrial smart grid, in this article, we construct an efficient identity-based metering data aggregation scheme supporting batch verification by collector and electricity service provider, respectively, which guarantees the privacy and integrity of metering data. In our scheme, collectors are allowed to collect and aggregate the metering data of users in their respective administrative domain without compromising the confidentiality of metering data. Security analysis demonstrates that our proposed scheme is provably secure in the random oracle and satisfies the above security requirements. Performance analysis indicates that our scheme outperforms existing solutions in terms of communication and computation costs.

Energieflexibilität im Industrial Smart Grid*/Energy Flexibility in Industrial Smart Grids – Methodical approach for incorporating energy flexibility measures in an Industrial Smart Grid

Durch den Übergang in Smart Grids erhalten Stromnetze die notwendige Flexibilität, um auf die steigende Volatilität der Stromversorgung zu reagieren. Das standardisierte Smart-Grid-Architecture-Modell und die Use-Case-Methodik dienen dabei als Rahmen, um Smart Grids zu entwickeln. Ein methodischer Rahmen für den Aufbau von Industrial Smart Grids fehlt derzeit noch, ist aber besonders notwendig, um Energieflexibilitätsmaßnahmen für Industrieanlagen umzusetzen. Der hier vorgestellte methodische Ansatz liefert eine Blaupause für den Aufbau eines Industrial Smart Grid für Energieflexibilität. Mithilfe der Anpassung des Smart-Grid-Architecture-Modells und der Use-Case-Methodik wird ein Industrial-Smart-Grid-Architecture-Modell umgesetzt.   Through their transition into Smart Grids, electrical power grids gain the neccesary flexibiltiy to respond to increasing electrical supply volatility. The standardized Smart Grid Architecture Model and Use Case Methodology serve as framework to develop Smart Grids. Currently, there is still a lack of such a methodical framework for setting up Industrial Smart Energy Grids and it is particularly necessary to implement energy flexibility measures in industrial plants. The methodical approach presented here provides a blueprint for building an Industrial Smart Grid for energy flexibility by adapting the Smart Grid Architecture Model and the use case methodology .

Power Measurement and Data Logger with High-Resolution for Industrial DC-Grid Application

Abstract Power and energy measurement and monitoring is a key factor for many industries in terms of energy and cost efficiency evaluation. Due to trends of Smart Grid concept application in industrial environment, including decentralized DC-Grid implementation, for precise evaluation – faster and low-cost measurement equipment is needed. Manufacturing industry widely uses industrial robots that have dynamic load characteristics for which faster measurement equipment is needed. This paper gives a brief description of the developed power measurement equipment, its structure and interconnection with industrial Profinet network. Further as a testing method steady state and dynamic loads are selected and analyzed. For testing, specially created industrial DC-Grid testing environment and equipment was used. Testing results show that the selected method and idea is working and is able to measure dynamic loads with high resolution. For other industrial load types there is a discussion going on about the issue of how detailed the resolution is needed in industrial SmartGrids, as energy forecast is a new trend in robotic industry and manufacturing planning.

Reactive Power Control of Reversible Electric Drives by Using Industrial Smart Grid Technology

This paper addresses the issue of associating a concept of industrial smart grid technology for a reactive power control of reversible electric drives. The research focus was concerned with the theoretical analysis of active, reactive and apparent power by the main reversible electric drive of plate mill rolling stand. Typical features of power electric equipment of modern reversible electric drives allow us to develop the reactive power control. The reactive power control can be created out by using of back to back converters which connect the reversible electric drive with the supply mains. Experiments in the plate mill were carried at the difficult-to-form steel grade in the range of roughing and finish rolling. It was established that the reactive power generation can be used in the range of the roughing. The huge prospects of the reversible electric drives integration based on back to back converters in Industrial Smart Grid have been determined due to the fact that to control of reactive and apparent power flows.

Энергосберегающий электропривод на базе двухзвенного преобразователя частоты с активным выпрямителем и автономным инвертором напряжения

Functional possibilities of electric drive on base of back to back converter were considered from the point of view of energy saving. The studies were carried out by mathematical modeling in the Matlab/Simulink program. On the basis of studies on the simulation model the controlling of power factor of PWM boost rectifier can be used for reactive power compensation in AC mains were determined. Back to back converters can be actively used in operation of supply power system of enterprise as they are able to provide controlled power factor and bidirectional power flow. The control system of PWM boost rectifier can be used to realize four-quadrant controlling of electric drive. Integration prospects of electric drives based on back to back converters in an industrial smart grid were defined.