Distributed generation control using modified PLL based on proportional-resonant controller

Due to the increasing necessity for electrical demand, the microgrids (MGs) based on distributed generations (DGs) within power electronic interfaces are being extended to improve the traditional network control. One of the common ways to achieve the power sharing among the resources on an islanding MG is to use droop control approach, performing based on proportional-integrator (PI) controllers. However, due to the effect of feeder impedance, obtaining the reactive power sharing using this method is not accurate and leads to overload in some DGs, results in the output terminal voltage of each DG go out of the allowable range. The second problem arises when the frequency measurement is not performed precisely, leading inaccurate active power sharing, which can be solved by using an improved phase locked loop (PLL). Therefore, the purpose of this paper is to propose an applicable and simple approach based on the use of conventional droop characteristics and a proportional-resonant (PR) controller in a DG control system. Due to the load changes in the microgrid and other contingencies, the proposed PLL-based controller is able to represent supreme performance with low error in several case studies

Spatial‐temporal learning structure for short‐term load forecasting

Abstract In the power system operational/planning studies, it is a crucial task to provide the load consumption information in the look‐ahead times. The huge variation of the power system infrastructure in recent years has led to significant changes in the consumers’ consumption pattern. Therefore, short‐term load forecasting (STLF) is transformed to a more complicated problem in recent years. To address this issue, this paper proposes a graph‐based deep neural network to capture full spatial‐temporal features and be able to oversee high volatility time series including load sequence. The proposed spatial deep learning structure benefits from learning the spatial feature using Gabor filter‐oriented layers and full understanding the temporal behaviour based on bidirectional networks. The designed learning‐based system is developed as a graph‐based learning system to improve the accuracy considering the meteorological information behaviour. To verify the performance of the designed deep graph network, the actual load data of Shiraz, Iran, is used. Besides, to demonstrate the superiority and effectiveness of the proposed, the designed deep graph network is compared with three well‐known shallow and deep networks in different cases including yearly performance, seasonal performance, and sensitivity analysis on the meteorological data

Droop Method Development for Microgrids Control Considering Higher Order Sliding Mode Control Approach and Feeder Impedance Variation

Due to the growing power demands in microgrids (MGs), the necessity for parallel production achieved from distributed generations (DGs) to supply the load required by customers has been increased. Since the DGs have to procure the demand in parallel mode, they are faced with several technical and economic challenges, such as preventing DGs overloading and not losing network stability considering feeder impedance variation. This paper presents a method that upgrades the droop controller based on sliding mode approach, so that DGs are able to prepare a suitable reactive power sharing without error even in more complex MGs. In the proposed strategy, the third-order sliding mode controller significantly reduces the V-Q error and increases the accuracy in adjusting the voltage at the DG output terminals. Various case studies conducted out in this paper validate the truthfulness of the proposed method, considering the stability analysis using Lyapunov function. Finally, by comparing the control parameters of the proposed technique with existing methods, the superiority, simplicity and effectiveness of the 3rd order sliding mode control (SMC) method are determinedPeer reviewe