Rapid Fault Analysis by Deep Learning-Based PMU for Smart Grid System

Smart Grids(SG)is a power system development concept that has received significant attention nationally.SG signifies real-time data for specific communication requirements.The best capabilities for monitoring and controlling the grid are essential to system stability.One of the most critical needs for smart-grid execution is fast,precise,and economically synchronized measurements,which are made feasible by Phasor Measurement Units(PMU).PMUs can pro-vide synchronized measurements and measure voltages as well as current phasors dynamically.PMUs utilize GPS time-stamping at Coordinated Universal Time(UTC)to capture electric phasors with great accuracy and precision.This research tends to Deep Learning(DL)advances to design a Residual Network(ResNet)model that can accurately identify and classify defects in grid-connected systems.As part of fault detection and probe,the proposed strategy uses a ResNet-50 tech-nique to evaluate real-time measurement data from geographically scattered PMUs.As a result of its excellent signal classification efficiency and ability to extract high-quality signal features,its fault diagnosis performance is excellent.Our results demonstrate that the proposed method is effective in detecting and classifying faults at sufficient time.The proposed approaches classify the fault type with a precision of 98.5%and an accuracy of 99.1%.The long-short-term memory(LSTM),Convolutional Neural Network(CNN),and CNN-LSTM algo-rithms are applied to compare the networks.Real-world data tends to evaluate these networks.

Review of Fault Types, Impacts, and Management Solutions in Smart Grid Systems

Fault management study in smart grid systems (SGSs) is important to ensure the stability of the system. Also, it is important to know the major types of power failures for the effective operation of the SGS. This paper reviews diverse types of faults that might appear in the SGS and gives a survey about the impact of renewable energy resources (RERs) on the behavior of the system. Moreover, this paper offers different fault detection and localization techniques that can be used for SGSs. Furthermore, a potential fault management case study is proposed in this paper. The SGS model in this paper is investigated using both of the Matlab/Simulink and the Real Time Digital Simulation (RTDS) to compute the fault management study. Simulation results show the fast response to a power failure in the system which improves the stability of the SGS.

Optimal Management of Energy Storage Systems for Peak Shaving in a Smart Grid

In this paper,the installation of energy storage systems(EES)and their role in grid peak load shaving in two echelons,their distribution and generation are investigated.First,the optimal placement and capacity of the energy storage is taken into consideration,then,the charge-discharge strategy for this equipment is determined.Here,Genetic Algorithm(GA)and Particle Swarm Optimization(PSO)are used to calculate the minimum and maximum load in the network with the presence of energy storage systems.The energy storage systems were utilized in a distribution system with the aid of a peak load shaving approach.Ultimately,the battery charge-discharge is managed at any time during the day,considering the load consumption at each hour.The results depict that the load curve reached a constant state by managing charge-discharge with no significant changes.This shows the significance of such matters in terms of economy and technicality.

Data encryption based on a 9D complex chaotic system with quaternion for smart grid

With the development of smart grid, operation and control of a power system can be realized through the power communication network, especially the power production and enterprise management business involve a large amount of sensitive information, and the requirements for data security and real-time transmission are gradually improved. In this paper, a new 9-dimensional(9D) complex chaotic system with quaternion is proposed for the encryption of smart grid data. Firstly, we present the mathematical model of the system, and analyze its attractors, bifurcation diagram, complexity,and 0–1 test. Secondly, the pseudo-random sequences are generated by the new chaotic system to encrypt power data.Finally, the proposed encryption algorithm is verified with power data and images in the smart grid, which can ensure the encryption security and real time. The verification results show that the proposed encryption scheme is technically feasible and available for power data and image encryption in smart grid.

Technologies Behind the Smart Grid and Internet of Things: A System Survey

Electric smart grids enable a bidirectional flow of electricity and information among power system assets.For proper monitoring and con-trolling of power quality,reliability,scalability and flexibility,there is a need for an environmentally friendly system that is transparent,sustainable,cost-saving,energy-efficient,agile and secure.This paper provides an overview of the emerging technologies behind smart grids and the internet of things.The dependent variables are identified by analyzing the electricity consumption patterns for optimal utilization and planning preventive maintenance of their legacy assets like power distribution transformers with real-time parameters to ensure an uninterrupted and reliable power supply.In addition,the paper sorts out challenges in the traditional or legacy electricity grid,power generation,transmission,distribution,and revenue management challenges such as reduc-ing aggregate technical and commercial loss by reforming the existing manual or semi-automatic techniques to fully smart or automatic systems.This article represents a concise review of research works in creating components of the smart grid like smart metering infrastructure for postpaid as well as in prepaid mode,internal structure comparison of advanced metering methods in present scenarios,and communication systems.

An Efficient MPPT Tracking in Solar PV System with Smart Grid Enhancement Using CMCMAC Protocol

Renewable energy sources like solar,wind,and hydro are becoming increasingly popular due to the fewer negative impacts they have on the environment.Because,Since the production of renewable energy sources is still in the process of being created,photovoltaic(PV)systems are commonly utilized for installation situations that are acceptable,clean,and simple.This study presents an adaptive artificial intelligence approach that can be used for maximum power point tracking(MPPT)in solar systems with the help of an embedded controller.The adaptive method incorporates both the Whale Optimization Algorithm(WOA)and the Artificial Neural Network(ANN).The WOA was implemented to enhance the process of the ANN model’s training,and the ANN model was developed using the WOA.In addition to this,the inverter circuit is connected to the smart grid system,and the strengthening of the smart grid is achieved through the implementation of the CMCMAC protocol.This protocol prevents interference between customers and the organizations that provide their utilities.Using a protocol known as Cross-Layer Multi-Channel MAC(CMCMAC),the effect of interference is removed using the way that was suggested.Also,with the utilization of the ZIGBEE communication technology,bidirectional communication is made possible.The strategy that was suggested has been put into practice,and the results have shown that the PV system produces an output power of 73.32 KW and an efficiency of 98.72%.In addition to this,a built-in regulator is utilized to validate the proposed model.In this paper,the results of various experiments are analyzed,and a comparison is made between the suggested WOA with the ANN controller approach and others,such as the Particle Swarm Optimization(PSO)based MPPT and the Cuckoo Search(CS)based MPPT.By examining the comparison findings,it was determined that the adaptive AI-based embedded controller was superior to the other alternatives.

Intelligent Smart Grid Stability Predictive Model for Cyber-Physical Energy Systems

A cyber physical energy system(CPES)involves a combination of pro-cessing,network,and physical processes.The smart grid plays a vital role in the CPES model where information technology(IT)can be related to the physical system.At the same time,the machine learning(ML)modelsfind useful for the smart grids integrated into the CPES for effective decision making.Also,the smart grids using ML and deep learning(DL)models are anticipated to lessen the requirement of placing many power plants for electricity utilization.In this aspect,this study designs optimal multi-head attention based bidirectional long short term memory(OMHA-MBLSTM)technique for smart grid stability predic-tion in CPES.The proposed OMHA-MBLSTM technique involves three subpro-cesses such as pre-processing,prediction,and hyperparameter optimization.The OMHA-MBLSTM technique employs min-max normalization as a pre-proces-sing step.Besides,the MBLSTM model is applied for the prediction of stability level of the smart grids in CPES.At the same time,the moth swarm algorithm(MHA)is utilized for optimally modifying the hyperparameters involved in the MBLSTM model.To ensure the enhanced outcomes of the OMHA-MBLSTM technique,a series of simulations were carried out and the results are inspected under several aspects.The experimental results pointed out the better outcomes of the OMHA-MBLSTM technique over the recent models.

Design and Development of an Intelligent Energy Management System for a Smart Grid to Enhance the Power Quality

MigroGrid(MG)has emerged to resolve the growing demand for energy.But because of its inconsistent output,it can result in various power quality(PQ)issues.PQ is a problem that is becoming more and more important for the reliability of power systems that use renewable energy sources.Similarly,the employment of nonlinear loads will introduce harmonics into the system and,as a result,cause distortions in the current and voltage waveforms as well as low power quality issues in the supply system.Thus,this research focuses on power quality enhancement in the MG using hybrid shunt filters.However,the performance of the filter mainly depends upon the design,and stability of the controller.The efficiency of the proposed filter is enhanced by incorporating an enhanced adaptive fuzzy neural network(AFNN)controller.The performance of the proposed topology is examined in a MATLAB/Simulink environment,and experimental findings are provided to validate the effectiveness of this approach.Further,the results of the proposed controller are compared with Adaptive Fuzzy Back-Stepping(AFBS)and Adaptive Fuzzy Sliding(AFS)to prove its superiority over power quality improvement in MG.From the analysis,it can be observed that the proposed system reduces the total harmonic distortion by about 1.8%,which is less than the acceptable limit standard.

An Intelligent Intrusion Detection System in Smart Grid Using PRNN Classifier

Typically,smart grid systems enhance the ability of conventional power system networks as it is vulnerable to several kinds of attacks.These vulnerabil-ities might cause the attackers or intruders to collapse the entire network system thus breaching the confidentiality and integrity of smart grid systems.Thus,for this purpose,Intrusion detection system(IDS)plays a pivotal part in offering a reliable and secured range of services in the smart grid framework.Several exist-ing approaches are there to detect the intrusions in smart grid framework,however they are utilizing an old dataset to detect anomaly thus resulting in reduced rate of detection accuracy in real-time and huge data sources.So as to overcome these limitations,the proposed technique is presented which employs both real-time raw data from the smart grid network and KDD99 dataset thus detecting anoma-lies in the smart grid network.In the grid side data acquisition,the power trans-mitted to the grid is checked and enhanced in terms of power quality by eradicating distortion in transmission lines.In this approach,power quality in the smart grid network is enhanced by rectifying the fault using a FACT device termed UPQC(Unified Power Quality Controller)and thereby storing the data in cloud storage.The data from smart grid cloud storage and KDD99 are pre-pro-cessed and are optimized using Improved Aquila Swarm Optimization(IASO)to extract optimal features.The probabilistic Recurrent Neural Network(PRNN)classifier is then employed for the prediction and classification of intrusions.At last,the performance is estimated and the outcomes are projected in terms of grid voltage,grid current,Total Harmonic Distortion(THD),voltage sag/swell,accu-racy,precision,recall,F-score,false acceptance rate(FAR),and detection rate of the classifier.The analysis is compared with existing techniques to validate the proposed model efficiency.

Physics-Informed AI Surrogates for Day-Ahead Wind Power Probabilistic Forecasting with Incomplete Data for Smart Grid in Smart Cities

Due to the high inherent uncertainty of renewable energy,probabilistic day-ahead wind power forecasting is crucial for modeling and controlling the uncertainty of renewable energy smart grids in smart cities.However,the accuracy and reliability of high-resolution day-ahead wind power forecasting are constrained by unreliable local weather prediction and incomplete power generation data.This article proposes a physics-informed artificial intelligence(AI)surrogates method to augment the incomplete dataset and quantify its uncertainty to improve wind power forecasting performance.The incomplete dataset,built with numerical weather prediction data,historical wind power generation,and weather factors data,is augmented based on generative adversarial networks.After augmentation,the enriched data is then fed into a multiple AI surrogates model constructed by two extreme learning machine networks to train the forecasting model for wind power.Therefore,the forecasting models’accuracy and generalization ability are improved by mining the implicit physics information from the incomplete dataset.An incomplete dataset gathered from a wind farm in North China,containing only 15 days of weather and wind power generation data withmissing points caused by occasional shutdowns,is utilized to verify the proposed method’s performance.Compared with other probabilistic forecastingmethods,the proposed method shows better accuracy and probabilistic performance on the same incomplete dataset,which highlights its potential for more flexible and sensitive maintenance of smart grids in smart cities.

An evaluation model for smart grids in support of smart cities based on the Hierarchy of Needs Theory

Smart cities depend highly on an intelligent electrical networks to provide a reliable,safe,and clean power supplies.A smart grid achieves such aforementioned power supply by ensuring resilient energy delivery,which presents opportunities to improve the cost-effectiveness of power supply and minimize environmental impacts.A systematic evaluation of the comprehensive benefits brought by smart grid to smart cities can provide necessary theoretical fundamentals for urban planning and construction towards a sustainable energy future.However,most of the present methods of assessing smart cities do not fully take into account the benefits expected from the smart grid.To comprehensively evaluate the development levels of smart cities while revealing the supporting roles of smart grids,this article proposes a model of smart city development needs from the perspective of residents’needs based on Maslow’s Hierarchy of Needs theory,which serves the primary purpose of building a smart city.By classifying and reintegrating the needs,an evaluation index system of smart grids supporting smart cities was further constructed.A case analysis concluded that smart grids,as an essential foundation and objective requirement for smart cities,are important in promoting scientific urban management,intelligent infrastructure,refined public services,efficient energy utilization,and industrial development and modernization.Further optimization suggestions were given to the city analyzed in the case include strengthening urban management and infrastructure constructions,such as electric vehicle charging facilities and wireless coverage.

An Ethereum-based solution for energy trading in smart grids

The need for a flexible,dynamic,and decentralized energy market has rapidly grown in recent years.As a matter of fact,Industry 4.0 and Smart Grids are pursuing a path of automation of operations to insure all the steps among consumers and producers are getting closer.This leads towards solutions that exploit the paradigm of public blockchain,which represents the best platform to design flat and liquid markets for which providing trust and accountability to mutual interactions becomes crucial.On the other hand,one of the risks arising in this situation is that personal information is exposed to the network,with intolerable threats to privacy.In this paper,we propose a solution for energy trading,based on the blockchain Ethereum and Smart Contracts.The solution aims to be a concrete proposal to satisfy the needs of energy trading in smart grids,including the important feature that no information about the identity of the peers of the network is disclosed in advance.

A Distributed Power Trading Scheme Based on Blockchain and Artificial Intelligence in Smart Grids

As an emerging hot technology,smart grids(SGs)are being employed in many fields,such as smart homes and smart cities.Moreover,the application of artificial intelligence(AI)in SGs has promoted the development of the power industry.However,as users’demands for electricity increase,traditional centralized power trading is unable to well meet the user demands and an increasing number of small distributed generators are being employed in trading activities.This not only leads to numerous security risks for the trading data but also has a negative impact on the cost of power generation,electrical security,and other aspects.Accordingly,this study proposes a distributed power trading scheme based on blockchain and AI.To protect the legitimate rights and interests of consumers and producers,credibility is used as an indicator to restrict untrustworthy behavior.Simultaneously,the reliability and communication capabilities of nodes are considered in block verification to improve the transaction confirmation efficiency,and a weighted communication tree construction algorithm is designed to achieve superior data forwarding.Finally,AI sensors are set up in power equipment to detect electricity generation and transmission,which alert users when security hazards occur,such as thunderstorms or typhoons.The experimental results show that the proposed scheme can not only improve the trading security but also reduce system communication delays.

A Blockchain-Based Architecture for Securing Industrial IoTs Data in Electric Smart Grid

There are numerous internet-connected devices attached to the industrial process through recent communication technologies,which enable machine-to-machine communication and the sharing of sensitive data through a new technology called the industrial internet of things(IIoTs).Most of the suggested security mechanisms are vulnerable to several cybersecurity threats due to their reliance on cloud-based services,external trusted authorities,and centralized architectures;they have high computation and communication costs,low performance,and are exposed to a single authority of failure and bottleneck.Blockchain technology(BC)is widely adopted in the industrial sector for its valuable features in terms of decentralization,security,and scalability.In our work,we propose a decentralized,scalable,lightweight,trusted and secure private network based on blockchain technology/smart contracts for the overhead circuit breaker of the electrical power grid of the Al-Kufa/Iraq power plant as an industrial application.The proposed scheme offers a double layer of data encryption,device authentication,scalability,high performance,low power consumption,and improves the industry’s operations;provides efficient access control to the sensitive data generated by circuit breaker sensors and helps reduce power wastage.We also address data aggregation operations,which are considered challenging in electric power smart grids.We utilize a multi-chain proof of rapid authentication(McPoRA)as a consensus mechanism,which helps to enhance the computational performance and effectively improve the latency.The advanced reduced instruction set computer(RISC)machinesARMCortex-M33 microcontroller adopted in our work,is characterized by ultra-low power consumption and high performance,as well as efficiency in terms of real-time cryptographic algorithms such as the elliptic curve digital signature algorithm(ECDSA).This improves the computational execution,increases the implementation speed of the asymmetric cryptographic algorithm and provides data integrity and device authenticity at the perceptual layer.Our experimental results show that the proposed scheme achieves excellent performance,data security,real-time data processing,low power consumption(70.880 mW),and very low memory utilization(2.03%read-only memory(RAM)and 0.9%flash memory)and execution time(0.7424 s)for the cryptographic algorithm.This enables autonomous network reconfiguration on-demand and real-time data processing.

Optimizing Decision-Making of A Smart Prosumer Microgrid Using Simulation

Distributed renewable energy sources offer significant alternatives for Qatar and the Arab Gulf region’s future fuel supply and demand.Microgrids are essential for providing dependable power in difficult-to-reach areas while incorporating significant amounts of renewable energy sources.In energy-efficient data centers,distributed generation can be used to meet the facility’s overall power needs.This study primarily focuses on the best energy management practices for a smart microgrid in Qatar while taking demandside load management into account.This article looked into a university microgrid in Qatar that primarily aimed to get all of its energy from the grid.While diesel generators are categorized as a dispatchable distributed generation with energy storage added to handle solar radiation from the sun and high grid power operating costs in the suggested scenario,wind turbines and solar Photovoltaic(PV)are classified as non-dispatchable distributed generators.The resulting linear math issues are assessed and displayed in MATLAB optimization software using a mixed-integer linear programming(MILP)strategy.According to the simulation results,the suggested energy management strategy reduced the university microgrid’s grid power costs by 38.8%,making it an affordable solution which is somehow greater than the prior case scenario’s 23%savings.The installed solar system capacity’s effects on the economy,society,and finances were also assessed,and it became clear that the best option for the smart microgrid was determined that would be 325 kW of solar PV,25 kW of wind turbine,and 600 kW of diesel generators,respectively.Given the current situation,university administrators are urged to participate in distributed generators and adopt cutting-edge designs for energy storage technologies due to the significant environmental and financial benefits.

Effect of electromagnetic disturbance on the practical QKD system in the smart grid

To improve the security of the smart grid, quantum key distribution（QKD） is an excellent choice. The rapid fluctuations on the power aerial optical cable and electromagnetic disturbance in substations are two main challenges for implementation of QKD. Due to insensitivity to birefringence of the channel, the stable phase-coding Faraday–Michelson QKD system is very practical in the smart grid. However, the electromagnetic disturbance in substations on this practical QKD system should be considered. The disturbance might change the rotation angle of the Faraday mirror, and would introduce an additional quantum bit error rate（QBER）. We derive the new fringe visibility of the system and the additional QBER from the electromagnetic disturbance. In the worst case, the average additional QBER only increases about 0.17% due to the disturbance, which is relatively small to normal QBER values. We also find the way to degrade the electromagnetic disturbance on the QKD system.

Ensemble Voting-Based Anomaly Detection for a Smart Grid Communication Infrastructure

Advanced Metering Infrastructure(AMI)is the metering network of the smart grid that enables bidirectional communications between each consumer’s premises and the provider’s control center.The massive amount of data collected supports the real-time decision-making required for diverse applications.The communication infrastructure relies on different network types,including the Internet.This makes the infrastructure vulnerable to various attacks,which could compromise security or have devastating effects.However,traditional machine learning solutions cannot adapt to the increasing complexity and diversity of attacks.The objective of this paper is to develop an Anomaly Detection System(ADS)based on deep learning using the CIC-IDS2017 dataset.However,this dataset is highly imbalanced;thus,a two-step sampling technique:random under-sampling and the Synthetic Minority Oversampling Technique(SMOTE),is proposed to balance the dataset.The proposed system utilizes a multiple hidden layer Auto-encoder(AE)for feature extraction and dimensional reduction.In addition,an ensemble voting based on both Random Forest(RF)and Convolu-tional Neural Network(CNN)is developed to classify the multiclass attack cate-gories.The proposed system is evaluated and compared with six different state-of-the-art machine learning and deep learning algorithms:Random Forest(RF),Light Gradient Boosting Machine(LightGBM),eXtreme Gradient Boosting(XGboost),Convolutional Neural Network(CNN),Long Short-Term Memory(LSTM),and bidirectional LSTM(biLSTM).Experimental results show that the proposed model enhances the detection for each attack class compared with the other machine learning and deep learning models with overall accuracy(98.29%),precision(99%),recall(98%),F_(1) score(98%),and the UNDetection rate(UND)(8%).

Machine Learning-based Electric Load Forecasting for Peak Demand Control in Smart Grid

Increasing energy demands due to factors such as population,globalization,and industrialization has led to increased challenges for existing energy infrastructure.Efficient ways of energy generation and energy consumption like smart grids and smart homes are implemented to face these challenges with reliable,cheap,and easily available sources of energy.Grid integration of renewable energy and other clean distributed generation is increasing continuously to reduce carbon and other air pollutants emissions.But the integration of distributed energy sources and increase in electric demand enhance instability in the grid.Short-term electrical load forecasting reduces the grid fluctuation and enhances the robustness and power quality of the grid.Electrical load forecasting in advance on the basic historical data modelling plays a crucial role in peak electrical demand control,reinforcement of the grid demand,and generation balancing with cost reduction.But accurate forecasting of electrical data is a very challenging task due to the nonstationary and nonlinearly nature of the data.Machine learning and artificial intelligence have recognized more accurate and reliable load forecastingmethods based on historical load data.The purpose of this study is to model the electrical load of Jajpur,Orissa Grid for forecasting of load using regression type machine learning algorithms Gaussian process regression(GPR).The historical electrical data and whether data of Jajpur is taken for modelling and simulation and the data is decided in such a way that the model will be considered to learn the connection among past,current,and future dependent variables,factors,and the relationship among data.Based on this modelling of data the network will be able to forecast the peak load of the electric grid one day ahead.The study is very helpful in grid stability and peak load control management.

Metaheuristic Optimization with Deep Learning Enabled Smart Grid Stability Prediction

Due to the drastic increase in global population as well as economy,electricity demand becomes considerably high.The recently developed smart grid(SG)technology has the ability to minimize power loss at the time of power distribution.Machine learning(ML)and deep learning(DL)models can be effectually developed for the design of SG stability techniques.This article introduces a new Social Spider Optimization with Deep Learning Enabled Statistical Analysis for Smart Grid Stability(SSODLSA-SGS)pre-diction model.Primarily,class imbalance data handling process is performed using Synthetic minority oversampling technique(SMOTE)technique.The SSODLSA-SGS model involves two stages of pre-processing namely data nor-malization and transformation.Besides,the SSODLSA-SGS model derives a deep belief-back propagation neural network(DBN-BN)model for the pre-diction of SG stability.Finally,social spider optimization(SSO)algorithm can be applied for determining the optimal hyperparameter values of the DBN-BN model.The design of SSO algorithm helps to appropriately modify the hyperparameter values of the DBN-BN model.A series of simulation analyses are carried out to highlight the enhanced outcomes of the SSODLSA-SGS model.The extensive comparative study reported the enhanced performance of the SSODLSA-SGS algorithm over the other recent techniques interms of several measures.

Smart-Grid Monitoring using IoT with Modified Lagranges Key Based Data Transmission

One of the recent advancements in the electrical power systems is the smart-grid technology.For the effective functioning of the smart grid,the process like monitoring and controlling have to be given importance.In this paper,the Wireless Sensor Network(WSN)is utilized for tracking the power in smart grid applications.The smart grid is used to produce the electricity and it is connected with the sensor to transmit or receive the data.The data is transmitted quickly by using the Probabilistic Neural Network(PNN),which aids in identifying the shortest path of the nodes.While transmitting the data from the smart grid to the(Internet of Things)IoT web page,it is secured by introducing the secret keys between the neighbouring nodes through the process of key-management.In this method,the combination of Lagrange’s theorem and the Location Based Key(LBK)management is used for better security performance.This approach deli-vers optimal performance in terms of security,throughput,packet loss and delay,which are comparatively better than the existing methods.