Software Package For Improving Financial And Technological Performance Of Microgrid Networks

In modern world, the use of the most advanced digital technologies in any industry and business directly effect on financial and technological indicators, in other words, the more advanced digital technologies are used to solve various tasks, the greater the profit or benefits that can be gained. The electric power industry is not an exception. Nowadays, more and more of the electric power sector is moving from large networks to small, often isolated, so-called Microgrid. Such networks generally have generation based on renewable energy sources (RE): wind power plants, solar power plants, small hydropower plants, tidal power plants, etc.In view of the fact that generation is stochastic, networks with generation relying on renewable energy sources have energy storage. It is also worth noting that isolated and non-isolated Microgrid even if generation does not base on renewable energy, energy storage devices can bring them a certain benefits connected with the changing cost of electricity during the day or year. Thus, a consumer of Microgrid has much greater capabilities than a consumer of a “traditional”, centralized power supply system, but he is also subject to much greater responsibility, because many of the functions that the system operator used to perform now fall on his shoulders. So, the following customer features in Microgrid can be distinguished: the ability to disconnect themselves from the mains supply for the period when consuming is not profitable for them, the ability to sell electricity to the power supply network, independently maintain equipment (including generating), calculate and forecast their consumption and generation, make profit from the sale of electricity to the network.Obviously, in the past, the average consumer was not capable meeting the greatest part of the needs of his own electrical “industry” independently, but using modem digital technologies, most of the tasks that previously were impossible to fulfill could be automated without the direct participation of the consumer. That is why, it is proposed to use software systems which will be based on neural networks. The task of these software systems is to collect and process monitoring data, each consuming or generating unit in Microgrid, to perform a large number of tasks. One of such tasks is classification and creation characteristics of generating and consuming equipment by collecting and analyzing data from Microgrid participants. The algorithm determines the characteristics of consumption and generation. Based on these characteristics under various external conditions and factors, load and generations schedules (especially important for renewable energy sources) and possible emergency events are predicted. In addition, such software systems make it possible to optimize the algorithms for determining the most profitable hours for consumption or selling of electricity to the network. It is more convenient to operate the described software systems as a cloud services. In other words, in order to start implement a software package into operation, the consumer will only need an Internet connection, so there is no need for computers with high computational abilities, all calculations occur remotely.This work describe a software package which include the automation, forecasting and optimization of the financial and technological performance of Microgrid networks. It considers the data that the software package needs for complete analysis and further prediction, methods and algorithms that underpin this software package and the possible benefit from its use. RTDS hardware and software system was used to model the power system; the prediction methodology was based on recurrent neural networks (RNN).Machine learningWhat exactly is machine learning? It is obvious that "learning" is when a certain model" learns"in a some way and then begins to return results, that is, most likely, to predict something. A very general definition of"learnability" is roughly the same as that given by Thomas Mitchell in his book “Machine learning" [4]: “A computer program is said to learn from experience with some class of tasks T and performance measure P, if its performance at tasks (as measured by P) improves with experience" [5].The main classification of machine learning tasks is shown in Fig. 1. The two main classes of machine learning tasks are supervised learning and unsupervised learning. To fulfill the purposes-to detect faults in power transformers on the basis of PMU, it is necessary to carry out supervised learning tasks such as data classification. In the work presented here, data is a set of features that will be fed to the neural network input with the expected output: “true"- interwinding fault occurrence, "false"- without inter-winding fault. In order for a trained neural network to accurately detect turn-to-turn faults in a transformer it is highly important to prepare a sufficient set of data and to select the features of this type of damage as accurately as possible.