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EN
The paper contains selected research results in the field of identification and metaidentification of the Day Ahead Market system of TGE S.A. Due to the proposed new approach to identification, a methodology for conducting research has been developed, which requires eight stages. Then, both the tasks and research objectives as well as the form of research occurring at all stages of research in order to meet the distinguished specific objectives and the general purpose of the research were shown in detail. Then an example of both identification and metaidentification of Day Ahead Market systems was shown. The obtained models and metamodels confirm the need and possibility of conducting this type of research at TGE S.A.
EN
The paper presents the results of the research on the comparative study of the methods of cluster analysis and conditions, which was carried out from the point of view of their use, on the example of data concerning the operation of the National Power System. Two algorithms were used for the clustering analysis, i.e. the Ward algorithm and the algorithm of self-organizing two-dimensional maps. Cluster analysis was preceded by a review of hierarchical and non-hierarchical methods of data analysis and a description of the prepared experiment. The obtained results were interpreted. The work consists of two parts published under the same main title with different subtitles. This part 1 presents the results of the conducted review of selected methods of cluster analysis and the research conditions resulting from the adopted data on the operation of the National Power System. Part 2 presents the cluster analysis process and selected research results and their discussion.
EN
The paper presents selected results of research on the use of artificial intelligence methods, which are inspired by quantum computing solutions for modelling of electric power exchange systems. Methods used in the modelling of quantum data acquisition, quantization and dequantization of information as well as the methods of performing quantum computations were emphasized. Furthermore, we have analysed the results obtained for the neural model and for the evolutionary algorithm inspired by the quantum computer science. Eventually, the model was verified on the example of the neural model of the Electric Power Exchange (EPE).
EN
The work contains selected results of research on the application of quantum computer science to a systemic evolutionary algorithm for the purpose of improving accuracy of neural models in electrical engineering and electrical power engineering. Artificial neural networks are used in neural modeling, which networks are designed and taught models of systems using available numerical data. Parameters of neural networks, and especially, elements of weight matrices, biases as well as parameters of activation functions may be improved using evolutionary algorithms. It seems that applying solutions offered by quantum computer science to systemic evolutionary algorithm, and especially, as regards creation of quantum initial population, quantum crossover and mutation operators as well as selection, considerably improves the accuracy of modelling, which was verified in MATLAB and Simulink environment using selected examples such as RP–02 robot’s arm movement, the development of the Polish Electrical Power Exchange (polish: TGEE) system, etc.
EN
The paper contains selected results of research on the Domestic Power System (DPS) as an unmanned factory. Models of the DPS system of the MISO type, obtained as a result of identification for 14 inputs and 4 outputs were presented. Particular attention was given to the identification and the assessment of the DPS development based on the systems and control theory. The obtained models of the DPS development were analyzed and discussed. The studies were obtained model of the development of the DPS, and on the basis of is knowledge of the structural and parametrical changes of system development. The model can be used to analyze the design and development of the system from the point of view of the growth of internal organization system and the transition to higher levels of control.
EN
The paper presents selected results of research on the design of artificial neural networks and training them using the electrical power system development model (EPS or EP system) based on IEEE RTS 96 test data, i.a. creation of training and test files, development of architecture of the artificial neural network, selection of parameters of the network, selection of appropriate training and testing method, etc. As a result of the development and training an ANN, the following EP system development models were obtained, which were examined for sensitivity to changes of the number of hidden layers, number of neurons in a layer, activation function, training method, etc. Subsequently, simulation models for studying fitness of the obtained models to the real systems. Interesting results were obtained, e.g. the method of the neural modelling of the system, the optimal architecture of the ANN that is a model of the system, possibilities and directions to improve a neural model of the system, etc.
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