Machine Learning Approach Application for High-voltage Instrument Transformers Technical State Assessment

This paper describes the possibilities of machine learning application in the tasks of technical state assessment of high-voltage instrument transformers. An analytical review of modern systems for technical state assessment of high-voltage equipment is presented, their advantages and disadvantages are described. A mathematical model of an automated system for assessing the high-voltage instrument current and voltage transformers based on gradient boosting over decision trees has been developed. The efficiency of the developed solution is proved using the example of analysis of a real distribution zone, which allows identifying the state of instrument transformers with an accuracy of 84%

Initial database creation for scientific and technical solutions efficiency assessment based on artificial intelligence approach

This paper is devoted to the problems and features of database creation in intelligent systems for assessing the efficiency of scientific and technical solutions. The system data model developed by the authors and the principles of its operation are described. This paper also considers the process of training sampling and the analysis of various teaching methods for solving the presented problem. The implementation of the developed model is made using mathematical modeling. The initial data was the data of applications for grants in the field of technical sciences related to the fuel and energy complex

Industry Experience of Developing Day-Ahead Photovoltaic Plant Forecasting System Based on Machine Learning

This article highlights the industry experience of the development and practical implementation of a short-term photovoltaic forecasting system based on machine learning methods for a real industry-scale photovoltaic power plant implemented in a Russian power system using remote data acquisition. One of the goals of the study is to improve photovoltaic power plants generation forecasting accuracy based on open-source meteorological data, which is provided in regular weather forecasts. In order to improve the robustness of the system in terms of the forecasting accuracy, we apply newly derived feature introduction, a factor obtained as a result of feature engineering procedure, characterizing the relationship between photovoltaic power plant energy production and solar irradiation on a horizontal surface, thus taking into account the impacts of atmospheric and electrical nature. The article scrutinizes the application of different machine learning algorithms, including Random Forest regressor, Gradient Boosting Regressor, Linear Regression and Decision Trees regression, to the remotely obtained data. As a result of the application of the aforementioned approaches together with hyperparameters, tuning and pipelining of the algorithms, the optimal structure, parameters and the application sphere of different regressors were identified for various testing samples. The mathematical model developed within the framework of the study gave us the opportunity to provide robust photovoltaic energy forecasting results with mean accuracy over 92% for mostly-sunny sample days and over 83% for mostly cloudy days with different types of precipitation

Weather data errors analysis in solar power stations generation forecasting

The paper presents a short-term forecasting model for solar power stations (SPS) generation developed by the authors. This model is based on weather data and built into the existing software product as a separate short-term forecasting module for the SPS generation. The main problems associated with forecasting the SPS generation on cloudy days were revealed in the framework of authors' research, which is due not to the error of the developed model but to the use of the same learning sample for both solar and cloudy days. This paper contains analysis of the main problems related to the learning sampling, samples pattern, quality and representativeness for forecasting the SPS generation on cloudy days. Besides, the paper includes a calculation example performed for the existing SPS and a detailed analysis of the forecast generation on cloudy days based on the actual weather provider data

Very-short term solar power generation forecasting based on trend-additive and seasonal-multiplicative smoothing methodology

In conditions of development of generating facilities on renewable energy sources, the technology runs up to uncertainty in the operational and short-term planning of the power system operating modes. To date, reliable tools for forecasting the generation of solar power stations are required. This paper considers the methodology of operational forecasting of solar power stations output based on the mathematical apparatus of cubic exponential smoothing with trend and seasonal components. The presented methodology was tested based on the measuring data of a real solar power station. The average forecast error was not more than 10% for days with variable clouds and not more than 3% for clear days, which indicates the effectiveness of the proposed approach

Very-short term solar power generation forecasting based on trend-additive and seasonal-multiplicative smoothing methodology

In conditions of development of generating facilities on renewable energy sources, the technology runs up to uncertainty in the operational and short-term planning of the power system operating modes. To date, reliable tools for forecasting the generation of solar power stations are required. This paper considers the methodology of operational forecasting of solar power stations output based on the mathematical apparatus of cubic exponential smoothing with trend and seasonal components. The presented methodology was tested based on the measuring data of a real solar power station. The average forecast error was not more than 10% for days with variable clouds and not more than 3% for clear days, which indicates the effectiveness of the proposed approach

Evaluation model for urban power supply systems

The present article deals with the issue of evaluation of state of urban power supply systems (hereinafter PSS). Such systems are characterized by large size and low reliability of the available data. The suggested evaluation model (EM) is based on elements of the fuzzy logic theory and on the informational graph structure of the power network. EM allows for evaluation of the PSS objects irrespective of their size. PSS object here stands for any part of the PSS, from power switch to complex PSS with substations and transmission lines of various voltage. Graph-based structure of the network model (NM) provides for connection between the objects and allows to select parts in necessary and sufficient detail. Evaluation is made by quantitative indicators that reflect performance of the objects in various spheres (technical, exploitation, ecological etc) with transition to qualitative states: normal, pre-emergency and emergency states. © (2012) Trans Tech Publications

Machine learning techniques for short-term solar power stations operational mode planning

The paper presents the operational model of very-short term solar power stations (SPS) generation forecasting developed by the authors, based on weather information and built into the existing software product as a separate module for SPS operational forecasting. It was revealed that one of the optimal mathematical methods for SPS generation operational forecasting is gradient boosting on decision trees. The paper describes the basic principles of operational forecasting based on the boosting of decision trees, the main advantages and disadvantages of implementing this algorithm. Moreover, this paper presents an example of this algorithm implementation being analyzed using the example of data analysis and forecasting the generation of the existing SPS

Machine learning techniques for short-term solar power stations operational mode planning

The paper presents the operational model of very-short term solar power stations (SPS) generation forecasting developed by the authors, based on weather information and built into the existing software product as a separate module for SPS operational forecasting. It was revealed that one of the optimal mathematical methods for SPS generation operational forecasting is gradient boosting on decision trees. The paper describes the basic principles of operational forecasting based on the boosting of decision trees, the main advantages and disadvantages of implementing this algorithm. Moreover, this paper presents an example of this algorithm implementation being analyzed using the example of data analysis and forecasting the generation of the existing SPS