scholarly journals The Awareness and Adoption of Artificial Intelligence for Effective Facilities Management in the Energy Sector

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Jonathan Oluwapelumi Mobayo ◽  
Ayooluwa Femi Aribisala ◽  
Saheed Olanrewaju Yusuf ◽  
Usman Belgore
Keyword(s):  
Artificial Intelligence ◽  
Power Systems ◽  
Power System ◽  
Data Science ◽  
Descriptive Analysis ◽  
Energy Sector ◽  
Facility Management ◽  
Cyber Attacks ◽  
Quantitative Methodology ◽  
Awareness Level

Digitalization and artificial intelligence (AI) have infiltrated most sectors of the economy, including the energy sector, where they have been extensively investigated. The aim of the study is primarily to assess the awareness of AI in facility management, and to identify the prospects and challenges of the adoption of AI in the energy sector. The study adopted the quantitative methodology approach, using a structured questionnaire to a sample size of 384 respondents. The questionnaire was administered to professionals such as mechanical, civil, electrical, computer, and mechatronics engineers, and project managers within the North-central geopolitical zone of Nigeria. Data gathered was analysed using descriptive analysis (mean value, weighted total, and relative importance index). The study based on findings concludes that there exists high awareness level about the concept of AI in the energy sector. However, regarding the awareness about some selected AI technologies, machine & deep learning, robotics, and speech recognition had high awareness level. The study also concludes that improved energy management, efficiency and transparency, remote reading of energy meters, and improved planning, operation & control of power systems were prevalent prospects of AI adoption. The major challenging factors to the adoption of AI in the Nigerian energy sector are outdated power system infrastructure, cellular technologies, lack of qualified experts and data science skills, and growing threat from cyber-attacks. The study recommends improved awareness and technical know-how of energy sector personnel, and provision of adequate power system infrastructure to provide stable power supply.

scholarly journals Artificial Intelligence-Based Power System Stabilizers for Frequency Stability Enhancement in Multi-machine Power Systems

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Aliyu Sabo ◽  
Noor Izzri Abdul Wahab ◽  
Mohammad Lutfi Othman ◽  
Mai Zurwatul Ahlam Mohd Jaffar ◽  
Hakan Acikgoz ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Power Systems ◽  
Power System ◽  
Frequency Stability ◽  
Power System Stabilizers ◽  
Stability Enhancement

scholarly journals Application of Artificial Intelligence Techniques in Reactive Power/Voltage Control of Power System

2014 ◽  
Vol 1 (1) ◽  
pp. 5
Author(s):  
Gehao Sheng ◽  
Guangyu Tu ◽  
Yi Luo
Keyword(s):  
Artificial Intelligence ◽  
Power Systems ◽  
Power System ◽  
Reactive Power ◽  
Fuzzy Theory ◽  
Voltage Control ◽  
Control Methods ◽  
Multi Agent System ◽  
Multi Agent ◽  
Artificial Neural Network Ann

<p>As one of the important constituents of power system automation, reactive power/voltage control possesses inherent characteristics of complexity, nonlinearity, inaccuracy and high requirement for control speed, parts of which are hard to be described by the traditional mathematical models or to be realized by routine control methods. The artificial intelligence (AI) techniques have intelligence feature which traditional method does not bear, so special attentions are paid to the application of AI techniques in reactive voltage control and a lot of results in this field are obtained. In this paper the main results and methods of applying the AI techniques, such as Expert System (ES), Artificial Neural Network (ANN), Fuzzy Theory (FT), Genetic Algorithm (GA) and Multi-Agent System (MAS), etc., to reactive voltage control in power systems are summarized, the respective application features of these techniques are analyzed and compared and some problems to be solved are pointed out.</p>

The Role of Artificial Intelligence and Fifth-Generation (5G) Technology in Mitigating Cyber Physical Attacks on Power Systems

2021 ◽  
pp. 424-441
Author(s):  
Kehinde Oluwafemi Olusuyi ◽  
Paul Kehinde Olulope ◽  
Abiodun Ernest Amoran ◽  
Eno Edet Peter
Keyword(s):  
Artificial Intelligence ◽  
Power Systems ◽  
Power System ◽  
Electric Power ◽  
New Technologies ◽  
Electric Power System ◽  
System A ◽  
Fifth Generation ◽  
Control Technologies ◽  
And Control

The present-day electric power system is an evolving cyber-physical system. Researchers and industry players in the energy world continue to deploy new technologies towards making the electric power system a smarter grid. This involves the integration of information, communication, and control technologies into the existing power grid in order to improve its stability, security, and operational efficiency. Reliance of the modern power system's applications such as state estimation, sequential control and data acquisition (SCADA) systems, phasor measurement units (PMUs), etc. on open communication technologies including the internet has exposed the smart grid to various vulnerabilities, threats, and cyber-physical attacks. This chapter seeks to exploit the robust synergy which exists between artificial intelligence (AI) and fifth-generation (5G) technology to mitigate these challenges. A comprehensive review of techniques which have hitherto proven efficient and/or effective in mitigating identified challenges was carried out with a view to availing researchers of future directions.

The Application of Artificial Intelligence in Power System State Estimation with Intelligent Materials

2012 ◽  
Vol 485 ◽  
pp. 131-135 ◽  
Author(s):  
Yun Jing Liu ◽  
Feng Wen Wang
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Power Systems ◽  
Power System ◽  
State Estimation ◽  
Real Time ◽  
System State ◽  
Power System State Estimation ◽  
Artificial Intelligence Techniques

With the development of power systems, the problem of security, stability and economics has become increasingly important. Reliable real-time data base is the foundation of analysis of the systems security and stability. Power system state estimation is used to build reliable real-time model of the power network. It has the on-line security analysis function. Power systems are large, complex systems containing highly nonlinear components. Therefore, traditional approaches often have difficulties in finding the optimal solution efficiently. Artificial intelligence techniques are being applied to a wide range of practical problems in power system. With their ability to some laws of nature and mimic human reasoning, AI techniques such as fuzzy logic and genetic algorithm seem to be more efficient in dealing with large systems and complex problems. Artificial intelligence techniques have been applied in power system applications. This paper presents a method of adaptive genetic algorithm and fuzzy logic applied in phasor measurement placement and bad data identification. And simulation is evaluated on IEEE 22-bus power system.

scholarly journals A Comprehensive Survey on the Impact of Renewable Energy Sources in Power System Operation and Control

2021 ◽  
Author(s):  
Vinay Kumar Tatikayala ◽  
Shishir Dixit
Keyword(s):  
Artificial Intelligence ◽  
Renewable Energy ◽  
Power Systems ◽  
Power System ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
System Operation ◽  
System Parameters ◽  
And Control ◽  
The Impact

Abstract The concern for huge increasing electricity demand, fossil fuel depletion, developed infrastructure reliability, carbon footprint reduction insisted the power utility companies to uptake RES (Renewable Energy Sources). The improved adoption of RES like wind energy and solar energy into the prevailing transmission and distribution networks led to several problems. These problems could be rectified by optimizing the power system parameters like frequency response, inertia, stability, battery usage, efficiency and power loss. This review hence provide a comprehensive analysis on the impact of renewable energy sources like wind and solar energy on power system operation and control in accordance with the major findings of the existing works. This review highlights the difficulties in the installation of solar and wind power with adoptable solutions. The challenges of power systems regarding the encoding of non-linearized function could be rectified by AI (Artificial Intelligence). The paper also insists the importance of artificial intelligence algorithm in the optimization of power system parameters. Artificial intelligence methods is useful for resolving various issues in power systems such as control, scheduling, forecasting etc. Few artificial algorithms such as Atom search optimization, Particle swarm optimization, Salp swarm optimization were investigated in this review for improving the performance of the power system. In spite of optimization analysis, the paper investigate various storage system types for improving the power system in accordance with cost, application and operation characteristics. Proper understanding of these systems is necessary for the future designing and hence through revision of state of art characteristics has been performed in this paper.

scholarly journals A Comprehensive Review of the Cyber-Attacks and Cyber-Security on Load Frequency Control of Power Systems

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3860
Author(s):  
Athira M. Mohan ◽  
Nader Meskin ◽  
Hasan Mehrjerdi
Keyword(s):  
Power Systems ◽  
Power System ◽  
Cyber Security ◽  
Distribution Systems ◽  
Frequency Control ◽  
Cyber Attacks ◽  
Load Frequency Control ◽  
Comprehensive Review ◽  
Load Frequency ◽  
The Stability

Power systems are complex systems that have great importance to socio-economic development due to the fact that the entire world relies on the electric network power supply for day-to-day life. Therefore, for the stable operation of power systems, several protection and control techniques are necessary. The power system controllers should have the ability to maintain power system stability. Three important quantities that should be effectively controlled to maintain the stability of power systems are frequency, rotor angle, and voltage. The voltage control in power systems maintains the voltage and reactive power within the required limits and the power factor control enhances the efficiency of power distribution systems by improving load power factors. Among various controls, the frequency control is the most time-consuming control mechanism of power systems due to the involvement of mechanical parts. As the control algorithms of frequency stabilization deliver control signals in the timescale of seconds, load frequency control (LFC) systems cannot handle complicated data validation algorithms, making them more vulnerable to disturbances and cyber-attacks. In addition, the LFC system has extended digital layers with open communication networks and is designed to operate with less human intervention. Moreover, the frequency fluctuation due to load change or cyber-attack in one area affects all other interconnected areas, and thus threatens the stability of the entire network. Due to these circumstances, research activities are still carried out in the field of frequency control and cyber-security. In this paper, a comprehensive review of the cyber-security of the LFC mechanism in the power system is presented. The highlights of the paper include the identification of attack points of different configurations of the LFC system, discussion of the attack strategies, formulation of various attack models, and a brief review of the existing detection and defense mechanisms against cyber-attacks on LFC.

About digital transformation of the transport industry in Russia

2020 ◽  
Vol 19 (3) ◽  
pp. 95-105
Author(s):  
Natalia V. Vysotskaya ◽  
T. V. Kyrbatskaya
Keyword(s):  
Artificial Intelligence ◽  
Business Model ◽  
Data Science ◽  
Digital Transformation ◽  
Digital Culture ◽  
Blockchain Technology ◽  
Community Approach ◽  
Non Linear ◽  
Transport Industry ◽  
Artificial Intelligence Systems

The article is devoted to the consideration of the main directions of digital transformation of the transport industry in Russia. It is proposed in the process of digital transformation to integrate the community approach into the company's business model using blockchain technology and methods and results of data science; complement the new digital culture with a digital team and new communities that help management solve business problems; focus the attention of the company's management on its employees and develop those competencies in them that robots and artificial intelligence systems cannot implement: develop algorithmic, computable and non-linear thinking in all employees of the company.

Markov Jump Linear System Analysis of a Microgrid Operating in Islanded and Grid Tied Modes

2020 ◽  
Author(s):  
Gilles Mpembele ◽  
Jonathan Kimball
Keyword(s):  
Monte Carlo ◽  
Power Systems ◽  
Power System ◽  
System Analysis ◽  
Differential Algebraic Equations ◽  
Algebraic Equations ◽  
Markov Jump ◽  
Numerical Application ◽  
Conditional Moments ◽  
Markov Jump Linear Systems

<div>The analysis of power system dynamics is usually conducted using traditional models based on the standard nonlinear differential algebraic equations (DAEs). In general, solutions to these equations can be obtained using numerical methods such as the Monte Carlo simulations. The use of methods based on the Stochastic Hybrid System (SHS) framework for power systems subject to stochastic behavior is relatively new. These methods have been successfully applied to power systems subjected to</div><div>stochastic inputs. This study discusses a class of SHSs referred to as Markov Jump Linear Systems (MJLSs), in which the entire dynamic system is jumping between distinct operating points, with different local small-signal dynamics. The numerical application is based on the analysis of the IEEE 37-bus power system switching between grid-tied and standalone operating modes. The Ordinary Differential Equations (ODEs) representing the evolution of the conditional moments are derived and a matrix representation of the system is developed. Results are compared to the averaged Monte Carlo simulation. The MJLS approach was found to have a key advantage of being far less computational expensive.</div>

Grasshopper Algorithm Optimized Fractional Order Fuzzy PID Frequency Controller for Hybrid Power Systems

2019 ◽  
Vol 12 (6) ◽  
pp. 519-531
Author(s):  
Deepak Kumar Lal ◽  
Ajit Kumar Barisal
Keyword(s):  
Power Systems ◽  
Power System ◽  
Fractional Order ◽  
Pid Controller ◽  
Load Frequency Control ◽  
Pid Controllers ◽  
Fuzzy Pid ◽  
Fuzzy Pid Controller ◽  
Hybrid Power ◽  
Increasing Demand

Background: Due to the increasing demand for the electrical power and limitations of conventional energy to produce electricity. Methods: Now the Microgrid (MG) system based on alternative energy sources are used to provide electrical energy to fulfill the increasing demand. The power system frequency deviates from its nominal value when the generation differs the load demand. The paper presents, Load Frequency Control (LFC) of a hybrid power structure consisting of a reheat turbine thermal unit, hydropower generation unit and Distributed Generation (DG) resources. Results: The execution of the proposed fractional order Fuzzy proportional-integral-derivative (FO Fuzzy PID) controller is explored by comparing the results with different types of controllers such as PID, fractional order PID (FOPID) and Fuzzy PID controllers. The controller parameters are optimized with a novel application of Grasshopper Optimization Algorithm (GOA). The robustness of the proposed FO Fuzzy PID controller towards different loading, Step Load Perturbations (SLP) and random step change of wind power is tested. Further, the study is extended to an AC microgrid integrated three region thermal power systems. Conclusion: The performed time domain simulations results demonstrate the effectiveness of the proposed FO Fuzzy PID controller and show that it has better performance than that of PID, FOPID and Fuzzy PID controllers. The suggested approach is reached out to the more practical multi-region power system. Thus, the worthiness and adequacy of the proposed technique are verified effectively.

Analysis between graph-based and Power Transfer Distribution Factors (PTDF)-based model reduction methods in Electric Power Systems

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Diego A. Monroy-Ortiz ◽  
Sergio A. Dorado-Rojas ◽  
Eduardo Mojica-Nava ◽  
Sergio Rivera
Keyword(s):  
Power Systems ◽  
Power System ◽  
Model Reduction ◽  
Electrical Power ◽  
Schur Complement ◽  
Power Transfer ◽  
Electrical Power System ◽  
Admittance Matrix ◽  
Test Beds ◽  
Power Transfer Distribution Factors

Abstract This article presents a comparison between two different methods to perform model reduction of an Electrical Power System (EPS). The first is the well-known Kron Reduction Method (KRM) that is used to remove the interior nodes (also known as internal, passive, or load nodes) of an EPS. This method computes the Schur complement of the primitive admittance matrix of an EPS to obtain a reduced model that preserves the information of the system as seen from to the generation nodes. Since the primitive admittance matrix is equivalent to the Laplacian of a graph that represents the interconnections between the nodes of an EPS, this procedure is also significant from the perspective of graph theory. On the other hand, the second procedure based on Power Transfer Distribution Factors (PTDF) uses approximations of DC power flows to define regions to be reduced within the system. In this study, both techniques were applied to obtain reduced-order models of two test beds: a 14-node IEEE system and the Colombian power system (1116 buses), in order to test scalability. In analyzing the reduction of the test beds, the characteristics of each method were classified and compiled in order to know its advantages depending on the type of application. Finally, it was found that the PTDF technique is more robust in terms of the definition of power transfer in congestion zones, while the KRM method may be more accurate.

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