Multiple placement of static power compensators to ensure voltage stability based on flow optimization algorithm

Abstract

Relevance: Particle Swarm Optimization (PSO) Algorithm is used for Static VAr Compensators (SVCs) planning in a large power system. The primary function of a SVC is to improve transmission system voltage, thereby enhancing the maximum power transfer limit. To enhance voltage stability, the problem is considered as a multiple goals optimization problem for maximizing a fuzzy performance index. The multi-objective VAr planning problem in large-scale power system can be solved by the fuzzified PSO.

Aim: To elevate the accuracy of electricity consumption forecasting at industrial enterprises by using artificial intelligence methods, specifically, artificial neural network techniques, including the Long-Short Term Memory (LSTM) approach.

Methods: When developing the forecasting model, artificial neural network techniques were adopted, with a particular emphasis on the Long-Short Term Memory (LSTM) method. For primary data processing, Gaussian distribution principles and normalization/scaling techniques were applied.

Results: Substantiated computationally by applying the proposed model based on the artificial neural network technique for forecasting electricity consumption of industrial enterprises. A significant advantage of this method is its capability for learning and adaptability to forecasting. Real-time computations demonstrate its successful implementation, attributed primarily to appropriate selection of input layers and mitigation of random variables.