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The necessity of power is growing in response to today's growing population. In order to reduce both real and reactive power losses, three devices have been combined in this research. The distributed generations (DGs) and static var compensator (SVC) with static synchronous compensator (STATCOM) play important role for reduction of power losses (both real and reactive power) and improvement of voltage profile. The research outcome presented here outlines that presents genetic algorithm (GA) may be utilised to improve power system performance and voltage profile (VP) of distribution networks by integrating DG, SVC and STATCOM planning properly. On a 37-bus distribution network, the proposed methodology has been applied. The results of this study are helpful to those focusing on the functionality of viable and upcoming electrical grids, as well as those working in the disciplines of DG, SVC and STATCOM integration for improved system performance. [ABSTRACT FROM AUTHOR]

• Recursive least squares and generalized regression neural network are used to build a real-time dynamic system model for adaptive control. • A static var compensator additional dual-channel damping controller structure is proposed for the dominant mode of low-frequency oscillation. • Static var compensator additional damping controller parameters tuning problem converted Markov decision process and a soft actor-critic algorithm is also introduced to solve the Markov decision process. As the access rate of wind energy in a power system has significantly increased, stabilizing the power system has become challenging. Among these challenges, low-frequency oscillation is one of the most harmful problems, effectively resolved by adding a damping controller according to the relevant properties of the low-frequency oscillation. However, the controller often fails to adapt to the constantly changing wind energy system owing to the lack of a targeted dynamic change strategy. Thus, to address this issue, an adaptive stabilization strategy that uses a static var compensator with an additional damping controller structure is proposed. Specifically, the entire power system is equivalently represented as a generalized regression neural network, with a deep reinforcement learning algorithm called soft actor-critic introduced to train the agent based on the generalized regression neural network model. After the training process, the agent can provide additional efficient static var compensator damping controller parameters under different operating conditions, vastly improving the system stability. Simulation results verify the improved performance using the proposed strategy compared to other optimization methods, regardless of whether the low-frequency oscillations were suppressed in the time or frequency domains. [ABSTRACT FROM AUTHOR]

One of the prominent problems in wind farms is voltage flicker emission. To prevent flicker emission or mitigate the impact as best as possible, a static VAr compensator (SVC) is a great candidate both economically and technically. However, SVCs cannot completely compensate the fast-changing reactive power due to delays caused by the reactive power calculation unit and the triggering fire angle of the SVC. This paper proposes a predictive control system for SVCs, by merging an additional predictive control block into the conventional control system. It is constructed based on deep neural networks, namely adaptive one-dimensional convolutional neural network (1D-CNN). The training process is conducted based on the adaptive learning weights process to enhance the prediction accuracy and training computational complexity of the 1D-CNN. Numerical results on the actual dataset in a wind farm in Manjil, Iran, have verified the forecasting accuracy and flicker mitigation of the proposed controller. [ABSTRACT FROM AUTHOR]