A Comprehensive Review of Recent Strategies on Automatic Generation Control/Load Frequency Control in Power Systems

Babu, Naladi Ram; Bhagat, Sanjeev Kumar; Saikia, Lalit Chandra; Chiranjeevi, Tirumalasetty; Devarapalli, Ramesh; Márquez, Fausto Pedro García

doi:10.1007/s11831-022-09810-y

Cited by 56 publications

(13 citation statements)

References 202 publications

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“…Adaptive Neural Fuzzy Inference System (ANFIS) for building LFC in multiconnected areas with energy storage systems, which may reduce both control time and frequency variation during active power system operation [8]. Artificial neural networks and fuzzy reasoning come together to make neurofuzzy systems [9]. This technology uses neural networks to learn and fuzzy logic to figure out what to do.…”

Section: Proposed Schemementioning

confidence: 99%

Novel Intelligence ANFIS Technique for Two-Area Hybrid Power System’s Load Frequency Regulation

Nireekshana,

Ramachandran,

Narayana

2024

E3S Web Conf.

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The main objective of Load Frequency Control (LFC) is to effectively manage the power output of an electric generator at a designated site, in order to maintain system frequency and tie-line loading within desired limits, in reaction to fluctuations. The adaptive neuro-fuzzy inference system (ANFIS) is a controller that integrates the beneficial features of neural networks and fuzzy networks. The comparative analysis of Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Proportional-Integral-Derivative (PID)-based methodologies demonstrates that the suggested ANFIS controller outperforms both the PID controller and the ANN controller in mitigating power and frequency deviations across many regions of a hybrid power system. Two systems are analysed and represented using mathematical models. The initial system comprises a thermal plant alongside photovoltaic (PV) grid-connected installations equipped with maximum power point trackers (MPPT). The second system comprises hydroelectric systems. The MATLAB/Simulink software is employed to conduct a comparative analysis of the outcomes produced by the controllers.

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Section: Proposed Schemementioning

confidence: 99%

Novel Intelligence ANFIS Technique for Two-Area Hybrid Power System’s Load Frequency Regulation

Nireekshana,

Ramachandran,

Narayana

2024

E3S Web Conf.

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“…Despite the significant progress made using AI for PV generation, different challenges must be addressed to be resolved by future research focused on promising techniques based on AI, e.g., Explainable Artificial Intelligence (XAI) and novel hybrid techniques that reduce the weaknesses of current ML techniques to afford new PV operational scenarios required to reach the competitiveness in the energy sector [9,10]. Different techniques for PV generation must work in a dynamic parameter environment, implying that agents cannot have complete awareness of the parameters and environment [11,12]. Some research in the literature reviewed the issue of software failures, that may lead to inaccurate approximations [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Techniques for the Photovoltaic System: A Systematic Review and Analysis for Evaluation and Benchmarking

Kumar,

Dubey,

Segovia Ramírez

et al. 2024

Arch Computat Methods Eng

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Novel algorithms and techniques are being developed for design, forecasting and maintenance in photovoltaic due to high computational costs and volume of data. Machine Learning, artificial intelligence techniques and algorithms provide automated, intelligent and history-based solutions for complex scenarios. This paper aims to identify through a systematic review and analysis the role of artificial intelligence algorithms in photovoltaic systems analysis and control. The main novelty of this work is the exploration of methodological insights in three different ways. The first approach is to investigate the applicability of artificial intelligence techniques in photovoltaic systems. The second approach is the computational study and analysis of data operations, failure predictors, maintenance assessment, safety response, photovoltaic installation issues, intelligent monitoring etc. All these factors are discussed along with the results after applying the artificial intelligence techniques on photovoltaic systems, exploring the challenges and limitations considering a wide variety of latest related manuscripts.

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“…Alternatively, any variations in network frequency can lead to instability and may end in serious damage to network infrastructure, including turbines, motors, transformers, and other related devices. Hence, to maintain the balance between power generation and consumption and to ensure that the network frequency maintains proximity to the required nominal value, specific control and safety procedures are used [1], [2], [3]. The initial control operation utilized to achieve this purpose is referred to as primary frequency control, while tertiary as well as secondary control act as additional controls in this respect [4], [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Adaptive PID Controller Design for Over-Frequency Control in Microgrid Using Honey Badger Algorithm

Yakout,

Dashtdar,

AboRas

et al. 2024

IEEE Access

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Secondary frequency control systems, such as Automatic Generation Control (AGC), are used in interconnected power grids. However, when a system failure causes systems to separate into zones (islands), AGC can no longer be used, and the primary frequency control is the only control available. Moreover, load changes may cause frequency drop in some areas and over-frequency in other areas. Therefore, the goal in this article will be to design a neural network-based proportional, integral, and derivative (PID) controller in the primary control architecture to control the over frequency condition. The proposed controller is adaptively optimized in two stages by the honey badger algorithm (HBA). In the first stage, the PID controller gain values are optimized by the HBA algorithm for different values of load loss. While in the second stage, a feed-forward artificial neural network (ANN) is trained to match the tie-line measured power to the corresponding optimized HBA-PID gains obtained in the first stage. Finally, the proposed controller is implemented on a two-area interconnected thermal power system. The proposed controller results qualitatively outperform one of the best tuning methods, the Ziegler-Nichols (ZN) approach, and they show that the proposed controller has better dynamic responses with minimal frequency deviations and fast settling time, creating and guaranteeing a margin of stability for the closed loop.

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A Comprehensive Review of Recent Strategies on Automatic Generation Control/Load Frequency Control in Power Systems

Cited by 56 publications

References 202 publications

Novel Intelligence ANFIS Technique for Two-Area Hybrid Power System’s Load Frequency Regulation

Novel Intelligence ANFIS Technique for Two-Area Hybrid Power System’s Load Frequency Regulation

Artificial Intelligence Techniques for the Photovoltaic System: A Systematic Review and Analysis for Evaluation and Benchmarking

Neural Network-Based Adaptive PID Controller Design for Over-Frequency Control in Microgrid Using Honey Badger Algorithm

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