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Improvement in the computational efficiency of a technique for assessing the reliability of electric power systems based on the Monte Carlo method

... reliability assessment, random states can be defined as a shortage and shortage-free ones. To assess the reliability of power systems using the Monte Carlo method, one should analyze only the state of the system with a shortage. We suggest the use of machine learning methods to eliminate or sort the shortage and shortage-free states. The paper demonstrates the effectiveness of two methods: a support vector machine and a random forest. It also shows their performance when the Monte Carlo and quasi-Monte ...

Теги: machine learning , monte carlo method , power system , random sequences , reliability , computational efficiency , decision trees , electric power systems , learning systems , reliability analysis , support vector machines , control problems , energy systems , free st
Раздел: ИСЭМ СО РАН
Optimal operation control of PV-biomass gasifier-diesel-hybrid systems using reinforcement learning techniques

Kozlov A.N., Tomin N.V., Sidorov D.N., Lora E.E.S., Kurbatsky V.G. Optimal operation control of PV-biomass gasifier-diesel-hybrid systems using reinforcement learning techniques // Energies. Vol.13. No.10. ID: 2632. 2020. DOI: 10.3390/en13102632 The importance of efficient utilization of biomass as renewable energy in terms of global warming and resource shortages are well known and documented. Biomass gasification is a promising power technology especially for decentralized energy systems. Decisive...

Теги: biomass , co2 reduction , machine learning , microgrids , mixed integer linear programming , operations research , optimization , reinforcement learning , diesel engines , dual fuel engines , fuels , gasification , global warming , hybrid systems , learning systems , ma
Раздел: ИСЭМ СО РАН
Toward zero-emission hybrid AC/DC power systems with renewable energy sources and storages: A case study from Lake Baikal region

Sidorov D., Panasetsky D., Tomin N., Karamov D., Zhukov A., Muftahov I., Dreglea A., Liu F., Li Y. Toward zero-emission hybrid AC/DC power systems with renewable energy sources and storages: A case study from Lake Baikal region // Energies. Vol.13. No.5. ID: 1226. 2020. DOI: 10.3390/en13051226 Tourism development in ecologically vulnerable areas like the lake Baikal region in Eastern Siberia is a challenging problem. To this end, the dynamical models of AC/DC hybrid isolated power system consisting...

Теги: forecasting , hybrid ac/dc power system , machine learning , renewable energy source , stochastic optimization , volterra models , deep learning , electric power generation , electric power transmission networks , energy storage , integral equations , lakes , optimiz
Раздел: ИСЭМ СО РАН
Multi-output regression in electric power systems adequacy assessment using monte-carlo method

Boyarkin D., Krupenev D., Iakubovskii D. Multi-output regression in electric power systems adequacy assessment using monte-carlo method // SIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings. ID: 8958279. 2019. P.690-694. ISBN (print): 9781728144016. DOI: 10.1109/SIBIRCON48586.2019.8958279 This paper deals with the computational efficiency related problem appearing in electric power systems adequacy assessment using Monte-Carlo method. When...

Теги: adequacy assessment , energy power system , machine learning , monte-carlo , computational efficiency , electric power systems , learning systems , nonlinear programming , computational experiment , effective approaches , non-linear optimiza
Раздел: ИСЭМ СО РАН
Recurrent Neural Networks Application to Forecasting with Two Cases: Load and Pollution

... learning, in the forecasting task. The forecasting models based on long short-term memory (LSTM) and gated recurrent unit (GRU) were established respectively, and the real data of power load and air pollution were verified. Compared with traditional machine learning algorithms, the simulation proves the superiority of the forecasting model based on RNN. © 2020, Springer Nature Switzerland AG. нет

Теги: deep learning , forecasting , gru , lstm , decision making , intelligent computing , learning algorithms , machine learning , pollution , forecasting modeling , forecasting models , forecasting problems , neural netwo
Раздел: ИСЭМ СО РАН
Energy balancing using charge/discharge storages control and load forecasts in a renewable-energy-based grids

Sidorov D., Tao Q., Muftahov I., Zhukov A., Karamov D., Dreglea A., Liu F. Energy balancing using charge/discharge storages control and load forecasts in a renewable-energy-based grids // Chinese Control Conference, CCC. Vol.2019-July. ID: 8865777. 2019. P.6865-6870. ISBN (print): 9789881563972. DOI: 10.23919/ChiCC.2019.8865777 Renewable-energy-based grids development needs new methods to maintain the balance between the load and generation using the efficient energy storages models. Most of the...

Теги: deep learning. , energy storage , forecasting , integral equations , inverse problem , machine learning , numerical methods , power systems , svm
Раздел: ИСЭМ СО РАН
Wind speed and power ultra short-term robust forecasting based on Takagi–Sugeno fuzzy model

... fuzzy model is obtained. Wind farms located in China (Shanxi Province) and in Ireland (County Kerry) are considered as cases with which to validate the proposed forecasting method. The forecasting results are compared with results from the contemporary machine learning-based models including support vector machine (SVM), the combined model of SVM and empirical mode decomposition, and back propagation neural network methods. The results show that the proposed T–S fuzzy model can effectively improve ...

Теги: linearization , machine learning , wind power: wind speed: t–s fuzzy model: forecasting , backpropagation , clustering algorithms , fuzzy clustering , learning systems , least squares approximations , neural networks , support vector machines , wind , wind power , b
Раздел: ИСЭМ СО РАН
Prediction of the power shortage in the electric power system by means of regression analysis by machine learning methods

Boyarkin D.A., Krupenev D.S., Iakubobsky D.V. Prediction of the power shortage in the electric power system by means of regression analysis by machine learning methods // E3S Web of Conferences. Vol.114. ID: 03003. 2019. DOI: 10.1051/e3sconf/201911403003 Modern electricity consumers place increasingly high demands on the level of reliability of power supply and, correspondingly, the reliability ...

Теги: decision trees , electric power systems , machine learning , number theory , random number generation , regression analysis , software reliability , support vector machines , electric power systems (eps) , mac
Раздел: ИСЭМ СО РАН
Intelligent control of a wind turbine based on reinforcement learning

Tomin N., Kurbatsky V., Guliyev H. Intelligent control of a wind turbine based on reinforcement learning // 2019 16th Conference on Electrical Machines, Drives and Power Systems, ELMA 2019 - Proceedings. ID: 8771645. 2019. ISBN (print): 9781728114132. DOI: 10.1109/ELMA.2019.8771645 Advanced controllers of modern wind turbines can help increase energy capture efficiency and reduce structural loading. However, to fulfill the modern wind turbine control demands with contradicting requirements (efficiency...

Теги: control , mimo control , pitch control , reinforcement learning , torque control , wind turbine , adaptive control systems , control engineering , electric machinery , machine learning , mimo systems , stochastic systems , wind , wind turbines , adaptive control des
Раздел: ИСЭМ СО РАН
The development of a joint modelling framework for operational flexibility in power systems

Voropai N., Rehtanz C., Kippelt S., Tomin N., Haeger U., Efimov D., Kurbatsky V., Kolosok I. The development of a joint modelling framework for operational flexibility in power systems // 2019 16th Conference on Electrical Machines, Drives and Power Systems, ELMA 2019 - Proceedings. ID: 8771685. 2019. ISBN (print): 9781728114132. DOI: 10.1109/ELMA.2019.8771685 The TU Dortmund University (Germany) and the Energy Systems Institute of the Russian Academy of Sciences (Russia) launched a joint research...

Теги: artificial intelligence , electric power system , flexibility , machine learning , power system security , electric machinery , electric power systems , learning systems , different layers , energy systems , modelling framework , operational flexibility , russian aca
Раздел: ИСЭМ СО РАН


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