Artificial Intelligence Techniques for Fault Location and Detection in Distributed Generation Power Systems

Dărab, Cosmin; Tarnovan, Radu; Turcu, Antoniu; Martineac, Corina

doi:10.1109/mps.2019.8759662

Cited by 17 publications

(10 citation statements)

References 18 publications

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“…Content may change prior to final publication. [101], [102], [103], [104], [105], [106], [67], [107], [108], [109], [110], [111] [119] reward using Q-learning technique by solving the following equation:…”

Section: Algorithms Based On Artificial Intelligence Approachesmentioning

confidence: 99%

A Review on Communication Aspects of Demand Response Management for Future 5G IoT- Based Smart Grids

2021

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In recent power grids, the need for having a two-way flow of information and electricity is crucial. This provides the opportunity for suppliers and customers to better communicate with each other by shifting traditional power grids to smart grids (SGs). In this paper, demand response management (DRM) is investigated as it plays an important role in SGs to prevent blackouts and provide economic and environmental benefits for both end-users and energy providers. In modern power grids, the development of communication networks has enhanced DRM programmes and made the grid smarter. In particular, with progresses in the 5G Internet of Things (IoT), the infrastructure for DRM programmes is improved with fast data transfer, higher reliability, increased security, lower power consumption, and a massive number of connections. Therefore, this paper provides a comprehensive review of potential applications of 5G IoT technologies as well as the computational and analytical algorithms applied for DRM programmes in SGs. The review holistically brings together sensing, communication, and computing (optimization, prediction), areas usually studied in a scattered way. A broad discussion on various DRM programmes in different layers of enhanced 5G IoT based SGs is given, paying particular attention to advances in machine learning (ML) and deep learning (DL) algorithms alongside challenges in security, reliability, and other factors that have a role in SGs' performance.INDEX TERMS Smart grid, demand response management, 5G , Internet of Things

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Section: Algorithms Based On Artificial Intelligence Approachesmentioning

confidence: 99%

A Review on Communication Aspects of Demand Response Management for Future 5G IoT- Based Smart Grids

2021

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“…In the conventional system, one of the techniques for isolating a failure unit of generations from the grid was the islanding method. Darab et al [67] deploy an AI technique to detect the fault and exact point of occurrence of a fault in DERs for rapid islanding of the affected unit.…”

Section: Res Integration: Generation Sidementioning

confidence: 99%

“…• Multi-agent system-based microgrid operation strategy for DR [42] • Distributed grid intelligence using FREEDM system to manage the DERs [52] • ANN for optimizing distributed grid operation [31] • Consensus-based distributed intelligence for optimizing SG control [50] • Optimization of distributed generation operation using GA [60] • ANN for forecasting local energy demand [55] • Central Information Model (CIM) for implementing VPP communication and control architecture in SCADA [42] • Deep Learning (Support Vector Regression (SVR), Recurrent Neural Network (RNN)) for electricity price forecasting [98] • Markov Decision Process and RL based smart energy community management [22] • Meta-heuristic algorithm for regulating voltage profile [43] • ANN for detecting energy fraud [28,67] • Multi-service energy storage for providing shared ownership of ESS between local network operator and customers [86] • ANN for DSM for smart consumers [31…”

Section: Introductionmentioning

confidence: 99%

State-of-the-Art Artificial Intelligence Techniques for Distributed Smart Grids: A Review

2020

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The power system worldwide is going through a revolutionary transformation due to the integration with various distributed components, including advanced metering infrastructure, communication infrastructure, distributed energy resources, and electric vehicles, to improve the reliability, energy efficiency, management, and security of the future power system. These components are becoming more tightly integrated with IoT. They are expected to generate a vast amount of data to support various applications in the smart grid, such as distributed energy management, generation forecasting, grid health monitoring, fault detection, home energy management, etc. With these new components and information, artificial intelligence techniques can be applied to automate and further improve the performance of the smart grid. In this paper, we provide a comprehensive review of the state-of-the-art artificial intelligence techniques to support various applications in a distributed smart grid. In particular, we discuss how artificial techniques are applied to support the integration of renewable energy resources, the integration of energy storage systems, demand response, management of the grid and home energy, and security. As the smart grid involves various actors, such as energy produces, markets, and consumers, we also discuss how artificial intelligence and market liberalization can potentially help to increase the overall social welfare of the grid. Finally, we provide further research challenges for large-scale integration and orchestration of automated distributed devices to realize a truly smart grid.

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“…The paper [9], presented the effect of DGs on power system protection and adaptive overcurrent protection's utilization for addressing such issues. In paper [10] author had suggested a few novel AI schemes for detecting the faults in systems. The author presented the research work in islanding detection, demerits, merits and control algorithms in this paper.…”

Section: Introductionmentioning

confidence: 99%

An Approach for Overcurrent Protection using PID Controller with Different Fault Condition

Nabi¹

2020

IJRASET

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A power system network generates, distributes and transmits the electricity. The reliable, as well as safe power systems, are very significant for successful operation. However, various losses and faults occur in power system which prevents the power system to work efficiently and also causes various damages. Overcurrent is a very critical fault as it results in extreme production of heat, and the possibility of fire or device damage, if not tripped timely. In order to protect the equipment from overcurrent, the overcurrent relays were designed. In the existing system, the Inverse Definite Minimum Time (IDMT) over-current relay was used for purpose of over-current protection. However, it consists of various drawbacks such as, they take more time to trip which can lead to damage in equipments. Also, the IDMT over-current relay faces the issue of fault severity variation. Thus, in order to overcome the previous issues, the PID based relay is designed in the proposed work which will perform fast tripping and perform well for different faulty conditions. In this paper, the simulation has been performed to ensure the efficiency of the proposed approach over the conventional one.

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Artificial Intelligence Techniques for Fault Location and Detection in Distributed Generation Power Systems

Cited by 17 publications

References 18 publications

A Review on Communication Aspects of Demand Response Management for Future 5G IoT- Based Smart Grids

A Review on Communication Aspects of Demand Response Management for Future 5G IoT- Based Smart Grids

State-of-the-Art Artificial Intelligence Techniques for Distributed Smart Grids: A Review

An Approach for Overcurrent Protection using PID Controller with Different Fault Condition

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