Smart Grid Applications Research Articles

Visual inspection of fault type and zone prediction in electrical grids using interpretable spectrogram-based CNN modeling

In electrical grids, fault diagnosis (fault type and fault location classifications) are critical due to their economic and important implications. Numerous smart grid applications have embraced data-driven methodologies. While the majority of the work in this topic has been on increasing the predicted accuracy of machine-learning model for fault diagnosis, one important aspect that has received less attention is the interpretability of these systems.We advocate for a complementary perspective. To represent faulty signals, we propose a spectrogram–convolutional neural network based representation of the electrical signals where pre-trained models such as GoogleNet and SqueezeNet are trivially used. We then perform multiple fault classification tasks and offer a visual interpretation of the collected findings. The suggested approach makes the model more transparent through the use of Gradient-weighted Class Activation Mapping (Grad-CAM), which visualizes regions in the input spectrogram that are more relevant for predictions, assisting the end-user in the understanding and interpreting the results. We explore the merits of the suggested technique in terms of increasing the transparency of the black-box machine learning system, which is a critical requirement for designing modernized smart grids.

Resource placement strategy optimization for smart grid application using 5G wireless networks

<p>With the evolution of 5G-network, wireless mobile networks are growing to take a strong stand in attempts to achieve ubiquitous large-scale acquisition, connectivity and processing. Smart-grids are among the critical areas that can benefit from the capabilities of the 5G-network, especially internet of things (IoT) applications such as massive machine-type-communications or ultra-reliable low-latency communications. A distributed cloud-services use the cloud, fog and edge computing infrastructures and applications to take advantage of every available resource including network equipment and connected objects to optimize cost, energy, and latency depending on the planned optimization criteria. In this article, we present smart-grid solution based on cloud-services and 5G-network, then we study the integration of smart-grid services in the cloud based on: placement in the cloud and in the end-device, and finally we introduce our proposed solution based on Intelligent placement strategy. The scenarios are evaluated by the iFogSim simulator, and the analyzed results compare the standard cloud placement, edge placement and our intelligent placement with regard to the optimization of the energy consumption, latency, and network usage. The findings show that cloud energy consumption can be substantially reduced using Intelligent Placement while respecting the potential central processing unit (CPU) processing power-limit for each IoT-device used and network constraints in smart-grid.</p>

Dynamic Role-Based Access Control Policy for Smart Grid Applications: An Offline Deep Reinforcement Learning Approach

Role-based access control (RBAC) is adopted in the information and communication technology domain for authentication purposes. However, due to a very large number of entities within organizational access control (AC) systems, static RBAC management can be inefficient, costly, and can lead to cybersecurity threats. In this article, a novel hybrid RBAC model is proposed, based on the principles of offline deep reinforcement learning (RL) and Bayesian belief networks. The considered framework utilizes a fully offline RL agent, which models the behavioral history of users as a Bayesian belief-based trust indicator. Thus, the initial static RBAC policy is improved in a dynamic manner through off-policy learning while guaranteeing compliance of the internal users with the security rules of the system. By deploying our implementation within the smart grid domain and specifically within a Distributed Energy Resources (DER) ecosystem, we provide an end-to-end proof of concept of our model. Finally, detailed analysis and evaluation regarding the offline training phase of the RL agent are provided, while the online deployment of the hybrid RL-based RBAC model into the DER ecosystem highlights its key operation features and salient benefits over traditional RBAC models.

Hybrid Deep Learning Applied on Saudi Smart Grids for Short-Term Load Forecasting

Despite advancements in smart grid (SG) technology, effective load forecasting utilizing big data or large-scale datasets remains a complex task for energy management, planning, and control. The Saudi SGs, in alignment with the Saudi Vision 2030, have been envisioned as future electrical grids with a bidirectional flow of power and data. To that end, data analysis and predictive models can enhance Saudi SG planning and control via artificial intelligence (AI). Recently, many AI methods including deep learning (DL) algorithms for SG applications have been published in the literature and have shown superior time series predictions compared with conventional prediction models. Current load-prediction research for the Saudi grid focuses on identifying anticipated loads and consumptions, on utilizing limited historical data and the behavior of the load’s consumption, and on conducting shallow forecasting models. However, little scientific proof on complex DL models or real-life application has been conducted by researchers; few articles have studied sophisticated large-scale prediction models for Saudi grids. This paper proposes hybrid DL methods to enhance the outcomes in Saudi SG load forecasting, to improve problem-relevant features, and to accurately predict complicated power consumption, with the goal of developing reliable forecasting models and of obtaining knowledge of the relationships between the various features and attributes in the Saudi SGs. The model in this paper utilizes a real dataset from the Jeddah and Medinah grids in Saudi Arabia for a full year, 2021, with a one-hour time resolution. A benchmark strategy using different conventional DL methods including artificial neural network, recurrent neural network (RNN), conventional neural networks (CNN), long short-term memory (LSTM), gated recurrent unit (GRU), and different real datasets is used to verify the proposed models. The prediction results demonstrate the effectiveness of the proposed hybrid DL models, with CNN–GRU and CNN–RNN with NRMSE obtaining 1.4673% and 1.222% improvements, respectively, in load forecasting accuracy.

Blockchain-Enabled Smart Grid Applications: Architecture, Challenges, and Solutions

The conventional electrical grid is undergoing substantial growth for reliable grid operation and for more efficient and sustainable energy use. The traditional grid is now metamorphosing into a smart grid (SG) that incorporates a diverse, heterogeneous blend of operating measures such as smart appliances, meters, and renewable energy resources. With better efficient results and dependability, the SG can be described as a modern electric power grid architecture. The SG is one of the greatest potential advances as a promising solution for the energy crisis. However, it is complex and its decentralization could be of tremendous benefit. Moreover, digitalization and integration of a large number of growing connections make it a target of cyber-attacks. In this sense, blockchain is a promising SG paradigm solution that offers several excellent features. There has been considerable effort put into using blockchains in the smart grid for its decentralization and enhanced cybersecurity; however, it has not been thoroughly studied in both application and architectural perspectives. An in-depth study was conducted on blockchain-enabled SG applications. Blockchain architectures for various applications, such as the synchrophasor applications, electric vehicles, energy management systems, etc., were proposed. The purpose of this article is to provide directions for future research efforts aimed at secure and decentralized SG applications using blockchain.

Framework for the design and automatic deployment of smart grid applications

Due to the rising complexity of application development in the smart grid domain, an integrated framework for the development of such applications is presented in this paper. The framework is based on the standards IEC 61499 and IEC 61850. While IEC 61850 allows the uniform system description of smart grid components, IEC 61499 provides a model for developing distributed applications. The idea of this framework is to encapsulate IEC 61850 functionality in function blocks from IEC 61499 to model distributed smart grid applications. The developed framework consists of three modules. Besides a module for describing the application using function blocks, a further module is responsible for distributing them to an existing device infrastructure. Thereby, it is possible to define constraints in order to find best solutions. Moreover, the structure of the smart grid is only specified in this module. Depending on the selected solution, the respective configuration files of the individual devices are generated automatically, which are then used for deploying the application to field devices. Using a specific use-case, the functionality of the framework was evaluated. In addition, it was deployed to a virtual test environment.

A zero power current and frequency sensor for smart grid applications

Obtaining real time feedback on the amplitude and frequency of current flowing through power lines is of significant importance to smart grids. This paper investigates the use of a zero power multifunctional device (sensor and energy harvester) to solve this challenging problem. The passive device combines a piezoelectric cantilever with a hard magnet mass in order to couple the AC magnetic field from the power line to the device to generate an oscillating kinetic force at the frequency of the current. The proposed device is passive, but the transmission of the data requires the use of wireless sensor nodes, which requires power. The piezoelectric cantilever acts as both a passive sensor and a kinetic energy harvester to power the wireless sensor nodes. This study investigates the effects on both the sensor sensitivity and energy harvesting power density as a function of the type of wire (stranded vs solid wires), stiffness of the cantilever, and pull strength of the magnet for high current applications. The results demonstrate that stranded wires have a slight reduction in sensitivity from 1.12 V A−1 to 0.98 V A−1 for solid and stranded wire. The stiffness of the cantilever also affects the sensitivity and energy harvesting performance, where stiffer beams resulted in high power density and sensitivity. Increasing the magnet thickness and pull strength resulted in increased power density and bandwidth but it also effects the optimal spatial location of the magnet in relation to the wire. The device was also validated as a frequency sensor with a resolution of <1 Hz, and could be further optimized in the future. The results of this study validate the potential use of the device as a multifunctional sensor/energy harvester for smart grid applications.

Simultaneous fault detection and control design for DC–AC converter with a neutral leg based on dynamic observer

In this paper, the problem of simultaneous fault detection and control (SFDC) for a 3-phase 4-wire converter with neutral bridge arm, which is commonly used in the application of smart grid and distributed generation, is considered. By employing a detector/controller module consisting of a dynamic observer and a state feedback controller based on the dynamic observer, a multi-objective H∞ optimization SFDC framework is established to achieve the fault detection and control objectives simultaneously. Extended linear matrix inequalities (LMIs) characterizations are obtained as the strict conditions for solvability of the SFDC problem. The proposed method can reduce the designing conservativeness and overcome the shortcoming of existing SFDC methods by applying the superiority of dynamic observer. The achievable performance is analyzed quantitatively and the effectiveness of our designing method was illustrated and verified both in simulation and the hardware-in-the-loop(HIL) simulation which is conducted in a real-time simulation platform built by StarSim and MT respectively.

Guest Editorial: Security and Privacy for Cloud-Assisted Internet of Things (IoT) and Smart Grid

Cloud computing has emerged as a new technological domain in the IT industry. Currently, several organizations working in various domains, such as healthcare, finance, manufacturing, smart grid, Internet of Things (IoT), and IT, are increasingly integrating cloud computing with their traditional applications. The key idea behind the usage of cloud computing in IoT is to increase efficiency without compromising the data quality. When it comes to collecting data of thousands or millions of servers, the cloud offers scalability and reduces the computational load on each sensor. The highly configured servers in the cloud are very useful in processing and analyzing the sensors’ data. The security of such a shared infrastructure is very crucial. It is the major barrier in the adoption of cloud-based services, followed by issues regarding compliance, privacy, and legal matters. The digitalization of critical infrastructures, such as smart grids (SGs), brings advantages and opportunities for remote access and control. It enables intelligent and online monitoring of these systems, which considerably enhances cyberattacks’ vulnerability. Cyberattacks are among the most important threats to SGs. Therefore, efficient control systems should be designed that can detect and isolate cyberattacks to keep the SG reliable and secure operation. This special section aims at providing a forum to discuss the most recent advances on security and privacy in cloud-assisted IoT and SG applications.

Probabilistic Modeling of Li-Ion Battery Remaining Useful Life

The estimation of lithium-ion batteries degradation plays an important role for the correct operation of smart grid and electric vehicle applications. In fact, only healthy battery energy storage systems meet minimum performance in terms of supplied voltage and power. Health prognostic is mandatory to ensure safe and reliable operation of batteries, as unsuccessful operation may cause technical/economic detriments or complete failures. The battery remaining useful life (RUL) depends on several random factors and thus it should be probabilistically characterized to allow the application of decision-making processes. In this article, a hybrid methodology is developed to characterize the RUL even with small amount of data, in view of the typical scarcity of data for adequately fitting the RUL probability distributions. Several distributions are considered in the methodology and compared for such purpose: the inverse Burr (IB) distribution, and mixtures of IB with inverse Gaussian and with inverse Weibull distributions. Expectation-maximization algorithms are specifically developed for the parameter estimation of the considered mixture distributions in the proposed methodology. The IB-based distributions are applied on a RUL dataset created by Monte Carlo sampling on an electrochemical battery model fitted upon given charge/discharge cycles. Numerical experiments are reported to assess the proposal with respect to benchmark distributions.

A Review – Home Renewable Energy Management Systems in Smart Grids

This paper presents a review on the Home Energy Management Systems (HEMS) for renewable energy production and used optimization methods. The HEMS is an important Smart Grid application. It is used to monitor and optimally manage the energy flows in buildings including renewable energy production, energy storage and smart home appliances. In this paper, two different methods for the optimal HEMS are selected and compared: Model Predictive Control (MPC) and Reinforcement Learning (RL). As a conclusion, the RL method can overcome the disadvantages of the MCP in the highly dynamic environment of buildings and renewable energies, and is a promising method for HEMS in Smart Grids. Finally, an experimental set-up of the hybrid renewable energy system is presented and its operation is discussed under the Time-of-Use energy management strategy.

Detection and Localization of PMU Time Synchronization Attacks via Graph Signal Processing

Time Synchronization Attacks (TSAs) against Phasor Measurement Units (PMUs) constitute a major threat to modern smart grid applications. By compromising the time reference of a set of PMUs, an attacker can change the phase angle of their measured phasors, with potentially detrimental impact on grid operation and control. Going beyond traditional residual-based techniques in detecting TSAs, in this paper we propose the use of Graph Signal Processing (GSP) to model the power grid so as to facilitate the detection and localization of TSAs. We analytically show that modeling the state of the power system as a low-pass graph signal can significantly improve the resilience of the grid against TSAs. We propose TSA detection and localization methods based on GSP, leveraging state-of-the-art machine learning algorithms. We provide empirical evidence for the efficiency of the proposed methods based on extensive simulations on five IEEE benchmark systems. In fact, our methods can detect at least 77% more TSAs of significant impact and localize an additional 70% of the attacked PMUs compared to state-of-the-art techniques.

Review on Interpretable Machine Learning in Smart Grid

In recent years, machine learning, especially deep learning, has developed rapidly and has shown remarkable performance in many tasks of the smart grid field. The representation ability of machine learning algorithms is greatly improved, but with the increase of model complexity, the interpretability of machine learning algorithms is worse. The smart grid is a critical infrastructure area, so machine learning models involving it must be interpretable in order to increase user trust and improve system reliability. Unfortunately, the black-box nature of most machine learning models remains unresolved, and many decisions of intelligent systems still lack explanation. In this paper, we elaborate on the definition, motivations, properties, and classification of interpretability. In addition, we review the relevant literature addressing interpretability for smart grid applications. Finally, we discuss the future research directions of interpretable machine learning in the smart grid.

WCCI/GECCO 2020 Competition on Evolutionary Computation in the Energy Domain: An overview from the winner perspective

Evolutionary computation is attracting attention in the energy domain as an alternative to tackle inherent mathematical complexity of some problems related to high-dimensionality, non-linearity, non-convexity, multimodality, or discontinuity of the search space. In this context, the research community launched the 2020 ”Competition on Evolutionary Computation in the Energy Domain: Smart Grid Applications” and an associated simulation framework to evaluate the performance of state-of-the-art evolutionary algorithms solving real-world problems. The competition includes two testbeds: (1) Day-ahead energy resource management problem in smart grids under uncertain environments; and (2) Bi-level optimization of end-users’ bidding strategies in local energy markets. This paper describes the general framework of the competition, the two testbeds, and the evolutionary algorithms that participated. A special section is dedicated to the winner approach, CUMDANCauchy++, a cellular Estimation Distribution Algorithm (EDA). A thorough analysis of the results reveals that, led by CUMDANCauchy++, the top three algorithms form a block of approaches all based on cellular EDAs. Moreover, for testbed 2, in which CUMDANCauchy++ did not achieve the best performance, the winner approach is also based on EDAs. The outcomes of the competition show that CUMDANCauchy++ is an effective algorithm solving both testbeds, and EDAs emerge as an algorithm class with promising performance for solving smart grid applications.

Retraction Note to: Design of ANFIS controller for intelligent energy management in smart grid applications

Retraction Note to: Design of ANFIS controller for intelligent energy management in smart grid applications

Precison Automated Measuring System for Accurate Comparison of Resistance Standards and Shunts

In this paper we will present a development of Measurement system for accurate comparison of low resistance standards and AC shunts ranging from 0.1 mΩ to 10 Ω as well as system testing and use. The system consists of a specially built current source, range selector, current reversal module and low cost analog to digital converter. The entire measurement procedure is automated and controlled by LabVIEW program. Subsequent testing has shown that the realized precision resistance measurement system has achieved precision comparable to more expensive commercial devices. The system or system elements can be used both in smart grid applications and in military laboratories for testing new technologie.

Performance of NOMA-Based Dual-Hop Hybrid Powerline-Wireless Communication Systems

The mixture of wireless and power line communications (PLC) is vital for implementing new applications in smart grid and vehicular communications. In this work, we investigate the performance of non-orthogonal multiple access (NOMA) based dual-hop hybrid communication systems with decode-and-forward relay. The wireless channel is characterized by Nakagami- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> fading under an additive white Gaussian noise (AWGN), while the PLC channels are characterized by Log-normal distribution with Bernoulli Gaussian noise including both background and impulsive noise components. New closed-form expressions for the outage probability, the asymptotic outage probability and ergodic capacity are derived and verified via extensive representative simulations. For more insights on the outage performance, we analyze the diversity order. Additionally, we proposed a power allocation optimization technique to achieve an outage-optimal performance. The results show that the system outage probability improves as the impulsive noise index and the arrival probability of the impulsive component of the PLC additive noise decrease, while their effect is negligible on the ergodic capacity. Finally, the performance of the proposed system is compared against a benchmark OMA-based system.

Power Electronics: My Life and Vision for the Future [My View

Power electronics has ushered in a new kind of industrial revolution in the 21st century because of its important roles in energy conservation, renewable energy systems (RESs), bulk energy storage, electric and hybrid electric vehicles, and smart grid applications besides its traditional role in high-efficiency energy systems. Future advances in power electronics will occur mainly in two directions: wide-bandgap (WBG) power semiconductor devices and complex smart grid systems.

Robust H∞$H_\infty$ dynamic output feedback control of the DC–AC converter with a neutral leg

This paper investigates the problem of robust H ∞ $H_\infty$ dynamic output feedback control of 3-phase 4-wire converter with neutral bridge arm in the application of smart grid and distributed generation. Based on Lyapunov functions, a novel sufficient Linear Matrix Inequality condition analysis was performed on the dynamic output feedback stabilisation controllers. Meanwhile, some auxiliary slack variables with special structure and well-established performance conditions are employed to reduce design conservatism. This topology along with the H ∞ $H_\infty$ controller can solve the DC link balancing problem and maintain the deviation between the neutral point and the midpoint of DC supply very small. The achievable performance is analysed quantitatively and the controller design method was conducted and verified both in MATLAB simulation and the real-time simulation platform built by StarSim and MT, respectively.

Research Development on Aqueous Ammonium‐Ion Batteries

AbstractAqueous rechargeable batteries (ARBs) are provided with the merits of low cost, inherent security, environmental friendliness, and excellent electrochemical properties. Among various aqueous battery systems, aqueous ammonium‐ion batteries (AIBs) have shown great potential in low‐cost energy storage systems for future large‐scale smart grid applications due to their unique advantages including resource affordability and quite competitive electrochemical performance and received extensive research attention during recent years. Their unique battery chemistry also brings new insights and understanding into exploration of electrode and battery design. However, research on aqueous AIBs is still in its infancy and there are still many scientific issues in AIBs system worth exploring. In this paper, the latest developments in electrode materials, electrolytes, and battery chemistry in AIBs are reviewed in detail timely. Then further challenges and opportunities are pointed out.