Adaptive composite frequency control of power systems using reinforcement learning

Mu, Chaoxu; Wang, Ke; Ma, Shihua; Chong, Zhiqiang; Ni, Zhen

doi:10.1049/cit2.12103

Cited by 12 publications

(7 citation statements)

References 46 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%

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%

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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“…Due to the escalating power demand (PD) and the high rate of carbon emissions from TPPs, the integration of RESs into existing power networks is of utmost importance [38,39]. According to the research, load frequency control is challenging because of uncertain power generation and variable load demands [40][41][42]. The designed version of the PSO approach was utilized to attain an optimal solution for various categories of EDPs that involved both TPPs and RESs.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal Solution of Environmental Economic Dispatch Problems Using QPGPSO-ω

et al. 2023

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The renewable energy sources (RESs)-based economic dispatch problem (EDP) is of vital importance for modern power systems. Environmental pollution, climatic degradation, and rapidly growing prices of continuously depleting fossil fuels have encouraged researchers to consider mechanisms for RES implementation and optimal operations. This paper presents a quasi-oppositional population-based global particle swarm optimizer with inertial weights (QPGPSO-ω) to solve environment friendly EDPs. The optimization technique is applied to solve the EDP under different scenarios including cases where only renewable energy sources (RESs) are used and the cases where combined emission–economic dispatch (CEED) problem is taken into account. The scenario for RESs includes a combination of six wind, five solar PV, and four biofuel systems for power generation. EDPs are considered without any constraints, and the variability of resources is depicted over time, along with the regional load-sharing dispatch (RLSD). The case of CEED considers ten thermal units with the valve point loading (VPL) effect and transmission losses. The results obtained by the proposed QPGPSO-ω algorithm are better than the reported results employing other optimization methods. This is shown by the lower costs achieved up to USD 8026.1439 for the case of only RES-based EDPs, USD 1346.8 for the case of RES-based EDPs with RLSD, and USD 111,533.59 for the case of CEED. Thus, the proposed QPGPSO-ω algorithm was effective in solving the various adopted power dispatch problems in power system.

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“…Voltage stability [175]- [178] Security and cascading failure assessment [179]- [182] Transient stability [183]- [186] Building-level event detection Occupancy and activity [187]- [190] Fault diagnosis in appliances [191]- [194] Optimal power flow End-to-end learning [195]- [198] Learning-augmented [199]- [202] Energy management & control Demand side management Heating/Cooling loads [203]- [206] Demand-response [207]- [210] Buildings [211]- [214] Communities [215]- [218] Microgrids [219]- [222] Load frequency control [223]- [226] Voltage control [227]- [230] Grid variables estimation/ identification Phase [231]- [234] Topology and lines parameters [235]- [238] State estimation [239]- [242] Voltage calculation [243]- [246] FIGURE 9: Further decomposition of analytics services (II). Four indicative references are given for each analytics category.…”

Section: Analytics Cybersecuritymentioning

confidence: 99%

Data-Driven Analytics for Reliability in the Buildings-to-Grid Integrated System Framework: A Systematic Text-Mining-Assisted Literature Review and Trend Analysis

Bachoumis,

Mylonas,

Plakas

et al. 2023

IEEE Access

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Data-driven machine learning-based methods have provided immense capabilities, revolutionizing sectors like the Buildings-to-grid (B2G) integrated system. Since the penetration rate of distributed energy resources increases towards a net-zero emissions power system, so does the need for advanced services that ensure B2G-integrated system reliability. The convergence of advancements in machine learning, computational resources at the entire cloud-edge continuum, and large datasets from sensing devices enable the development of these data-driven energy analytics services. This work conducts a systematic text-mining-based literature review to examine the diverse range and trends of machine learning methods used to enhance reliability in B2G-integrated systems. While traditional manual sampling and analysis approaches have limited the effectiveness of previous literature review papers in this field, this systematic literature review work aims to synthesize and summarize the existing body of research more efficiently and effectively. To achieve this, this study collected almost 10,500 papers from Scholar and Scopus databases. It employed text-mining-assisted BERTopic-based topic modelling and statistical trend analysis techniques to uncover semantic patterns and explore the temporal evolution of research themes. A two-dimensional taxonomy was derived to analyze the technical papers from a business and machine learning-related perspective. By quantifying the temporal trends within these topics, the study unveiled insights about the state-of-the-art analytics that ensure reliability in the B2G-integrated system domain while proposing future research directions.

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Adaptive composite frequency control of power systems using reinforcement learning

Cited by 12 publications

References 46 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

Optimal Solution of Environmental Economic Dispatch Problems Using QPGPSO-ω

Data-Driven Analytics for Reliability in the Buildings-to-Grid Integrated System Framework: A Systematic Text-Mining-Assisted Literature Review and Trend Analysis

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