Novel Neural Network-Based Load Frequency Control Scheme: A Case Study of Restructured Power System

Kumar, Nagendra; Mlik, Hasmat; Singh, Akhilesh Kumar; Alotaibi, Majed A.; Nassar, Mohamed

doi:10.1109/access.2021.3133360

Cited by 35 publications

(5 citation statements)

References 28 publications

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“…Testing was performed on IEEE39 and NRPG 246 models. In works [101][102][103][104][105], the RL, PSO, BBBC, GA, and DQN algorithms were used to solve the FC problem. In this section, 11 articles devoted to methods for selecting CAs for FC EPS based on ML algorithms were considered.…”

Section: Providing Acceptable Frequency Levelsmentioning

confidence: 99%

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Bulk Power Systems Emergency Control Based on Machine Learning Algorithms and Phasor Measurement Units Data: A State-of-the-Art Review

Senyuk,

Beryozkina,

Safaraliev

et al. 2024

Energies

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Modern electrical power systems are characterized by a high rate of transient processes, the use of digital monitoring and control systems, and the accumulation of a large amount of technological information. The active integration of renewable energy sources contributes to reducing the inertia of power systems and changing the nature of transient processes. As a result, the effectiveness of emergency control systems decreases. Traditional emergency control systems operate based on the numerical analysis of power system dynamic models. This allows for finding the optimal set of preventive commands (solutions) in the form of disconnections of generating units, consumers, transmission lines, and other primary grid equipment. Thus, the steady-state or transient stability of a power system is provided. After the active integration of renewable sources into power systems, traditional emergency control algorithms became ineffective due to the time delay in finding the optimal set of control actions. Currently, machine learning algorithms are being developed that provide high performance and adaptability. This paper contains a meta-analysis of modern emergency control algorithms for power systems based on machine learning and synchronized phasor measurement data. It describes algorithms for determining disturbances in the power system, selecting control actions to maintain transient and steady-state stability, stability in voltage level, and limiting frequency. This study examines 53 studies piled on the development of a methodology for analyzing the stability of power systems based on ML algorithms. The analysis of the research is carried out in terms of accuracy, computational latency, and data used in training and testing. The most frequently used textual mathematical models of power systems are determined, and the most suitable ML algorithms for use in the operational control circuit of power systems in real time are determined. This paper also provides an analysis of the advantages and disadvantages of existing algorithms, as well as identifies areas for further research.

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Section: Providing Acceptable Frequency Levelsmentioning

confidence: 99%

“…BBBC [103] 75-bus real power system --High performance, less overshoot, and fast damping of the regulation process.…”

Section: Pso [102] ---mentioning

confidence: 99%

Bulk Power Systems Emergency Control Based on Machine Learning Algorithms and Phasor Measurement Units Data: A State-of-the-Art Review

Senyuk,

Beryozkina,

Safaraliev

et al. 2024

Energies

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“…To meet Poolco and bilateral transactions in the restructured electricity market, a neural network-based internal model control system (NN-IMC) was considered as a secondary controller for the load frequency control problem (LFC) [252]. The test system is divided into four areas to carry out the analyses.…”

Section: Artificial Neutral Network (Ann)mentioning

confidence: 99%

“…Therefore, it was found that the NN-IMC control scheme performed well and effectively improved the system's responses. After subsequently carrying out a comparative analysis of the performances of the NN-IMC control scheme with those of the fractional-order PID (FO-PID) control scheme, the authors of [252] also conclude that the performance of the FO-PID controller is higher than that of the NN-IMC [252].…”

Section: Artificial Neutral Network (Ann)mentioning

confidence: 99%

A Comprehensive Review of Load Frequency Control Technologies

Rasolomampionona,

Połecki,

Zagrajek

et al. 2024

Energies

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Load frequency control (LFC) is one of the most important tools in power system control. LFC is an auxiliary service related to the short-term balance of energy and frequency of power systems. As such, it allows the acquisition of a central role in enabling electricity exchanges and providing better conditions. The classification of LFC can be carried out from different angles: we can enumerate, among others, the type of control used. The following types of control are presented in this review: classical, optimal, and robust control. More advanced controls can also be used for classification: fuzzy logic control, ANN control, genetic algorithms, PSO control, etc. The influence of renewables and power control tools like FACTS is also considered as a category to be analyzed. The last classifications are related to two important subjects—the influence of DC links on LFC efficiency and the dangers of cyberattacks on the LFC.

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“…There are several other algorithms particle swarm optimization (PSO), genetic algorithm (GA), imperialistic competition algorithm (ICA), harmony search (HS), are available in the literature. The authors used BBBC [13] to identify the best parameters of an FOPID controller, after comparing it to other search algorithms such as ICA and GA [14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Application of Fractional Order-PID Control Scheme in Automatic Generation Control of a Deregulated Power System in the Presence of SMES Unit

Kumar¹,

Alotaibi

Singh³

et al. 2022

Mathematics

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A fractional order PID (FOPID) control technique for automatic generation control (AGC) in a multi-area power system is presented in this study. To create a reliable controller, a variety of control strategies were used. The load frequency control (LFC) problem in a power system implementing different power transactions, such as bilateral and Poolco transactions, are investigated here. Because any control scheme’s performance is only as good as its parameters, the parameters of the designed control scheme were determined using the big bang big crunch (BBBC) algorithm. Furthermore, in this work, the effect of a superconductive magnetic energy storage (SMES) unit is addressed in the given test (two and four area) systems. When confronted with a fluctuation in immediate load, the SMES unit is thought to follow the initial drop in frequency and tie-line power in order to increase LFC. It is evident that the performance of an FOPID control scheme is improved in the presence of an SMES unit and it provides frequency, tie-line power, change in generation with reduced oscillations and settling time.

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Novel Neural Network-Based Load Frequency Control Scheme: A Case Study of Restructured Power System

Cited by 35 publications

References 28 publications

Bulk Power Systems Emergency Control Based on Machine Learning Algorithms and Phasor Measurement Units Data: A State-of-the-Art Review

Bulk Power Systems Emergency Control Based on Machine Learning Algorithms and Phasor Measurement Units Data: A State-of-the-Art Review

A Comprehensive Review of Load Frequency Control Technologies

Application of Fractional Order-PID Control Scheme in Automatic Generation Control of a Deregulated Power System in the Presence of SMES Unit

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