Deep learning algorithms and their relevance: A review

Nowadays, the most revolutionary area in computer science is deep learning algorithms and models. This paper discusses deep learning and various supervised, unsupervised, and reinforcement learning models. An overview of Artificial neural network(ANN), Convolutional neural network(CNN), Recurrent neural network (RNN), Long short-term memory(LSTM), Self-organizing maps(SOM), Restricted Boltzmann machine(RBM), Deep Belief Network (DBN), Generative adversarial network(GAN), autoencoders, long short-term memory(LSTM), Gated Recurrent Unit(GRU) and Bidirectional-LSTM is provided. Various deep-learning application areas are also discussed. The most trending Chat GPT, which can understand natural language and respond to needs in various ways, uses supervised and reinforcement learning techniques. Additionally, the limitations of deep learning are discussed. This paper provides a snapshot of deep learning

Dynamic Stability with Artificial Intelligence in Smart Grids

Environmental concerns are among the main drives of the energy transition in power systems. Smart grids are the natural evolution of power systems to become more efficient and sustainable. This modernization coincides with the vast and wide integration of energy generation and storage systems dependent on power electronics. At the same time, the low inertia power electronics, introduce new challenges in power system dynamics. In fact, the synchronisation capabilities of power systems are threatened by the emergence of new oscillations and the displacement of conventional solutions for ensuring the stability of power systems. This necessitates an equal modernization of the methods to maintain the rotor angle stability in the future smart grids. The applications of artificial intelligence in power systems are constantly increasing. The thesis reviews the most relevant works for monitoring, predicting, and controlling the rotor angle stability of power systems and presents a novel controller for power oscillation damping

Dynamic stability with artificial intelligence in smart grids

Environmental concerns are among the main drives of the energy transition in power systems. Smart grids are the natural evolution of power systems to become more efficient and sustainable. This modernization coincides with the vast and wide integration of energy generation and storage systems dependent on power electronics. At the same time, the low inertia power electronics, introduce new challenges in power system dynamics. In fact, the synchronisation capabilities of power systems are threatened by the emergence of new oscillations and the displacement of conventional solutions for ensuring the stability of power systems. This necessitates an equal modernization of the methods to maintain the rotor angle stability in the future smart grids. The applications of artificial intelligence in power systems are constantly increasing. The thesis reviews the most relevant works for monitoring, predicting, and controlling the rotor angle stability of power systems and presents a novel controller for power oscillation damping

Stacked-GRU Based Power System Transient Stability Assessment Method

With the interconnection between large power grids, the issue of security and stability has become increasingly prominent. At present, data-driven power system adaptive transient stability assessment methods have achieved excellent performances by balancing speed and accuracy, but the complicated construction and parameters are difficult to obtain. This paper proposes a stacked-GRU (Gated Recurrent Unit)-based transient stability intelligent assessment method, which builds a stacked-GRU model based on time-dependent parameter sharing and spatial stacking. By using the time series data after power system failure, the offline training is performed to obtain the optimal parameters of stacked-GRU. When the application is online, it is assessed by framework of confidence. Basing on New England power system, the performance of proposed adaptive transient stability assessment method is investigated. Simulation results show that the proposed model realizes reliable and accurate assessment of transient stability and it has the advantages of short assessment time with less complex model structure to leave time for emergency control