20 research outputs found
Power System Parameters Forecasting Using Hilbert-Huang Transform and Machine Learning
A novel hybrid data-driven approach is developed for forecasting power system
parameters with the goal of increasing the efficiency of short-term forecasting
studies for non-stationary time-series. The proposed approach is based on mode
decomposition and a feature analysis of initial retrospective data using the
Hilbert-Huang transform and machine learning algorithms. The random forests and
gradient boosting trees learning techniques were examined. The decision tree
techniques were used to rank the importance of variables employed in the
forecasting models. The Mean Decrease Gini index is employed as an impurity
function. The resulting hybrid forecasting models employ the radial basis
function neural network and support vector regression. Apart from introduction
and references the paper is organized as follows. The section 2 presents the
background and the review of several approaches for short-term forecasting of
power system parameters. In the third section a hybrid machine learning-based
algorithm using Hilbert-Huang transform is developed for short-term forecasting
of power system parameters. Fourth section describes the decision tree learning
algorithms used for the issue of variables importance. Finally in section six
the experimental results in the following electric power problems are
presented: active power flow forecasting, electricity price forecasting and for
the wind speed and direction forecasting
Probabilistic assessment of power system mode with a varying degree of wind sources integration
At present among renewable sources the wind and solar plants have the most significant portion of power generation. Randomly changing and intermittent nature of this power leads to the stochasticity of the power grid mode, estimation of parameters of which requires application of probabilistic modeling. In the paper it is proposed an advanced algorithm of probabilistic load flow based on the development of two-point estimation method, the efficiency of which is confirmed on the basis of computational experiments and comparative analysis of the Monte Carlo simulation results. Calculations and analysis of the modeling results were carried out on standard 14-nodal scheme of IEEE and real electrical network of “Azerenerji” Grid
Cyber security risks of interconnected information systems in intelligent management of microgrid communities
The paper examines ways to form energy communities, analyzes various management structures for such communities, assesses and identifies possible threats and vulnerabilities of information systems (IS), possible failures and failures in IS during cyberattacks, which can lead to errors in the formation of control actions. An approach to reducing the cybersecurity risks of the information infrastructure of the microgrid community is proposed
Improving the principles of short-term electric load forecasting of the Irkutsk region
Forecasting of electric load (EL) is an important task for both electric power entities and large consumers of electricity [1]. Large consumers are faced with the need to compose applications for the planned volume of EL, and the deviation of subsequent real consumption from previously announced leads to the appearance of penalties from the wholesale market. In turn, electricity producers are interested in forecasting the demand for electricity for prompt response to its fluctuations and for the purpose of optimal infrastructure development. The most difficult and urgent task is the hourly forecasting of EL, which is extremely important for the successful solution of problems of optimization of generating capacities, minimization of power losses, dispatching control, security assessment of power supply, etc. Ultimately, such forecasts allow optimizing the cash costs for electricity and fuel or water consumption during generation. This paper analyzes the experience of the branch of JSC "SO UPS" Irkutsk Regional Dispatch Office of the procedure for short-term forecasting of the EL of the Irkutsk region
Deep Reinforcement Learning for Energy Microgrids Management Considering Flexible Energy Sources
The problem of optimally activating the flexible energy sources (short- and long-term storage capacities) of electricity microgrid is formulated as a sequential decision making problem under uncertainty where, at every time-step, the uncertainty comes from the lack of knowledge about future electricity consumption and weather dependent PV production. This paper proposes to address this problem using deep reinforcement learning. To this purpose, a specific deep learning architecture has been used in order to extract knowledge from past consumption and production time series as well as any available forecasts. The approach is empirically illustrated in the case of off-grid microgrids located in Belgium and Russia
Improving the principles of short-term electric load forecasting of the Irkutsk region
Forecasting of electric load (EL) is an important task for both electric power entities and large consumers of electricity [1]. Large consumers are faced with the need to compose applications for the planned volume of EL, and the deviation of subsequent real consumption from previously announced leads to the appearance of penalties from the wholesale market. In turn, electricity producers are interested in forecasting the demand for electricity for prompt response to its fluctuations and for the purpose of optimal infrastructure development. The most difficult and urgent task is the hourly forecasting of EL, which is extremely important for the successful solution of problems of optimization of generating capacities, minimization of power losses, dispatching control, security assessment of power supply, etc. Ultimately, such forecasts allow optimizing the cash costs for electricity and fuel or water consumption during generation. This paper analyzes the experience of the branch of JSC "SO UPS" Irkutsk Regional Dispatch Office of the procedure for short-term forecasting of the EL of the Irkutsk region
Deep Reinforcement Learning for Energy Microgrids Management Considering Flexible Energy Sources
The problem of optimally activating the flexible energy sources (short- and long-term storage capacities) of electricity microgrid is formulated as a sequential decision making problem under uncertainty where, at every time-step, the uncertainty comes from the lack of knowledge about future electricity consumption and weather dependent PV production. This paper proposes to address this problem using deep reinforcement learning. To this purpose, a specific deep learning architecture has been used in order to extract knowledge from past consumption and production time series as well as any available forecasts. The approach is empirically illustrated in the case of off-grid microgrids located in Belgium and Russia
Stability of Power Grids: State-of-the-art and Future Trends
The state of the art of transient stability and steady-state (small signal) stability in power grids are reviewed. Transient stability concepts are illustrated with simple examples; in particular, we consider two machine learning-based methods for computing region of attraction: ROA produced by Neural Network Lyapunov Function; estimation of the ROA of IEEE 39-bus system using Gaussian process and Converse Lyapunov function. We discuss steady state stability in power systems, and using Prony’s modal analysis for evaluating small signal stability for the 7 Bus Test system and real French power system
Stability of intelligent energy system and intelligent control methods
In modern power systems, a variety of both objects and the tools of control is expected to be much larger than before. As a result, the dynamic properties of these systems are complicated, and the issues of maintaining stability come to the fore. The paper provides a brief overview of the types of stability, including those that, until recently, were considered local in the electric power systems of Russia. It is shown that in today’s conditions the violation of these types of stability affects the operation of the electric power system as a whole. Therefore, the coordination of control of both normal and emergency modes of the systems takes on a special role and should become more intelligent. In this regard, a brief overview of machine learning developments of control agents at different levels of the control hierarchy is presented