Artificial Intelligence-Based Secured Power Grid Protocol for Smart City

Abstract

Due to the modern power system’s rapid development, more scattered smart grid components are securely linked into the power system by encircling a wide electrical power network with the underpinning communication system. By enabling a wide range of applications, such as distributed energy management, system state forecasting, and cyberattack security, these components generate vast amounts of data that automate and improve the efficiency of the smart grid. Due to traditional computer technologies’ inability to handle the massive amount of data that smart grid systems generate, AI-based alternatives have received a lot of interest. Long Short-Term Memory (LSTM) and recurrent Neural Networks (RNN) will be specifically developed in this study to address this issue by incorporating the adaptively time-developing energy system’s attributes to enhance the model of the dynamic properties of contemporary Smart Grid (SG) that are impacted by Revised Encoding Scheme (RES) or system reconfiguration to differentiate LSTM changes & real-time threats. More specifically, we provide a federated instructional strategy for consumer sharing of power data to Power Grid (PG) that is supported by edge clouds, protects consumer privacy, and is communication-efficient. They then design two optimization problems for Energy Data Owners (EDO) and energy service operations, as well as a local information assessment method in Federated Learning (FL) by taking non-independent and identically distributed (IID) effects into consideration. The test results revealed that LSTM had a longer training duration, four hidden levels, and higher training loss than other models. The provided method works incredibly well in several situations to identify FDIA. The suggested approach may successfully induce EDOs to employ high-quality local models, increase the payout of the ESP, and decrease task latencies, according to extensive simulations, which are the last points. According to the verification results, every assault sample could be effectively recognized utilizing the current detection methods and the LSTM RNN-based structure created by Smart.

IoT Sensor Data Retrieval and Analysis in Cloud Environments for Enhanced Power Management

“…To seamlessly combine IoT file storage methods with current enterprise information systems, a paradigm with front, middle and back layers was provided. Given the maturity of current data technologies, data processors would welcome this strategy [19].…”

Fuzzy Clustering-Based Deep Learning for Short-Term Load Forecasting in Power Grid Systems Using Time-Varying and Time-Invariant Features

“…To optimize Θ, W is determined by the training dataset collected from the M existing users, namely D = [d (1) , d (2) , . .…”

“…The forecasting framework is shown in Figure 1. The DNN model, Θ, is developed by the training dataset, D, which contains the data from the M users, d (1) , d (2) , . .…”

“…Power grid systems supply power loads for millions of users which are dynamic and complex. Therefore, efficient and reliable power grid systems are essential for maintaining power stability and avoiding power system outages and supply user load demands without power interruptions [ 1 , 2 ]. A sufficient power utilization scheme with accurate short-term load forecasting (STLF) is necessary for application on power grid systems [ 3 , 4 , 5 ].…”

A Survey on the Use of Synthetic Data for Enhancing Key Aspects of Trustworthy AI in the Energy Domain: Challenges and Opportunities

“…Artificial Intelligence (AI) and Machine Learning (ML) models are considered to be a key factor in accomplishing the energy transition [5][6][7] and are already widely used in various areas of the energy sector. These areas include cybersecurity analysis and simulation for power system protection [8,9], simulation-based studies on grid stability, reliability, and resilience [10][11][12][13], simulation and analysis of smart grid technologies and architectures [14,15], as well as advanced simulation techniques for power system modeling and analysis [16,17]. Offering new and promising opportunities, AI technology continues to expand not only in the energy sector [18][19][20] but also in many other domains such as healthcare [21,22] and finance [23][24][25].…”