Non‐linear LS‐SVM with RBF‐kernel‐based approach for AGC of multi‐area energy systems

Panwar, Akhilesh; Nasiruddin, Ibraheem; Bansal, Ramesh C.

doi:10.1049/iet-gtd.2017.1402

Cited by 23 publications

(16 citation statements)

References 20 publications

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“…In a classical AGC scheme, proportional-integral (PI) controllers have widely been utilised to regulate the area control error (ACE) and frequency deviations [2] but the optimal PI parameters are difficult to be determined, especially when the system operating condition changes. In order to improve the control performance, many researches in improvements of AGC focus on establishing more accurate non-linear power system models [3][4][5] and applying more intelligent control approaches such as fuzzy logic [6], artificial neural networks [7][8][9][10], optimisation algorithms [11][12][13][14][15], and so forth.…”

Section: Background and Motivationmentioning

confidence: 99%

“…In practice, renewable energy sources are highly volatile in power output, which can cause critical challenges to AGC in both microgrids and interconnected power systems [16][17][18]. The control performance of existing research works [6][7][8][9][10][11][12][13][14][15][16][17][18] on AGC mostly depends on the estimated power mismatch, which is also the control signal obtained by disturbance observers. However, owing to the increased penetration of renewable energy sources and flexible loads, the fast and stochastic power fluctuations increase the difficulty in estimating the signal, and will significantly deteriorate the power system's frequency performance.…”

Section: Background and Motivationmentioning

confidence: 99%

“…In recent years, many frequency control methods [6][7][8][9][10][11][12][13][14][15][16][17][18] have been reported with the goal of enhancing the stability and reliability of the power network and improving efficiency. More specifically, Mohanty et al [6] deployed the fuzzy logic into an AGC for a multi-area power system to improve the dynamic performance.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Proactive frequency control based on ultra‐short‐term power fluctuation forecasting for high renewables penetrated power systems

Wen

Wang

Tang

et al. 2019

IET Renewable Power Generation

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The rapidly increasing penetration of renewable energies has introduced severe challenges to power system frequency controls due to the highly intermittent and uncertain power output of renewable energies. This study proposes a proactive frequency control method to control traditional synchronous generators in advance in anticipation of sudden power fluctuation. Therefore, the frequency deviations can be well mitigated by such early-acted control signal. In order to obtain this new control reference, an ensemble-forecasting model based on the extreme learning machine algorithm is designed to predict ultra-short-term power fluctuations, which serves as an extra signal for automatic generation control. The proposed method was verified on an equivalent model of the Singapore power system with various types of generations and loads. The simulation results clearly demonstrate the accuracy of the forecasting model and the advantages of the proposed control method. The proposed method can also reduce the frequency charging/discharging of energy storage systems, which can effectively extend their lifetime.

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Section: Background and Motivationmentioning

confidence: 99%

Section: Background and Motivationmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Proactive frequency control based on ultra‐short‐term power fluctuation forecasting for high renewables penetrated power systems

Wen

Wang

Tang

et al. 2019

IET Renewable Power Generation

View full text Add to dashboard Cite

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“…In order to minimize the topology, defining a fitting error function is necessary for solving the regression problem [45][46][47][48][49][50]:…”

Section: Least Squares Support Vector Machinementioning

confidence: 99%

“…One of the powerful tools that is highly utilized in the modelling process of the energy-conversion systems is machine learning. A recent method based on machine learning was introduced, Least Squares Support Vector Machine (LSSVM), which has been vastly used in modelling of engineering systems [42][43][44][45][46][47][48][49][50][51].…”

Section: Introductionmentioning

confidence: 99%

Current Status Investigation and Predicting Carbon Dioxide Emission in Latin American Countries by Connectionist Models

et al. 2019

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Currently, one of the biggest concerns of human beings is greenhouse gas emissions, especially carbon dioxide emissions in developed and under-developed countries. In this study, connectionist models including LSSVM (Least Square Support Vector Machine) and evolutionary methods are employed for predicting the amount of CO 2 emission in six Latin American countries, i.e., Brazil, Mexico, Argentina, Peru, Chile, Venezuela and Uruguay. The studied region is modelled based on the available input data in terms of million tons including oil (million tons), gas (million tons oil equivalent), coal (million tons oil equivalent), R e w (million tons oil equivalent) and Gross Domestic Product (GDP) in terms of billion U.S. dollars. Moreover, the available patents in the field of climate change mitigation in six Latin American countries, namely Brazil, Mexico, Argentina, Peru, Chile, Venezuela and Uruguay, have been reviewed and analysed. The results show that except Venezuela, all other mentioned countries have invested in renewable energy R&D activities. Brazil and Argentina have the highest share of renewable energies, which account for 60% and 72%, respectively.

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Adjustment strategy of a learning‐based faulty zone locator for distribution systems

Correa-Tapasco

Pérez-Londoño

Flórez

2021

Int Trans Electr Energ Syst

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Fault location is a functionality of the advanced distribution automation for smart grids. This functionality is considered in this paper using a learning-based faulty zone locator (LBFZL), which requires the adequate selection of its adjustment-variables. As these variables are strongly interrelated, the strategy proposed in this paper uses an iterative approach to determine their fair values based on sampling and learning theories. The proposed strategy guides the adjustment process considering the LBFZL performance. According to the analysed tests, the results present a successful selection of the five defined adjustment-variables, due to the high performance in locating single-phase, phase-to-phase, and three-phase faulty zones, in a 34.5 kV-75 bus distribution system. As a result of the proposal application, satisfactory values for the five adjustmentvariables are obtained; and finally, the expected LBFZL performance is higher than 95% for all fault types.

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Non‐linear LS‐SVM with RBF‐kernel‐based approach for AGC of multi‐area energy systems

Cited by 23 publications

References 20 publications

Proactive frequency control based on ultra‐short‐term power fluctuation forecasting for high renewables penetrated power systems

Proactive frequency control based on ultra‐short‐term power fluctuation forecasting for high renewables penetrated power systems

Current Status Investigation and Predicting Carbon Dioxide Emission in Latin American Countries by Connectionist Models

Adjustment strategy of a learning‐based faulty zone locator for distribution systems

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