Frequency control of the islanded microgrid including energy storage using soft computing

Abstract

Today, with the increasing penetration of microgrids, the degree of complexity and non-linearity of power systems has increased, causing conventional and inflexible controllers not to perform well in a wide range of operating points. In this paper, a self-tuning proportional-integral (PI)-controller based on a soft computation of a combination of genetic algorithm (GA) and artificial neural network (ANN). The GA-ANN is used to control the frequency of a microgrid in an island mode to automatically adjust and optimize the coefficients of a PI-controller. The proposed PI-controller is located in the frequency control secondary loop of an island microgrid. Since the ANN is a local search algorithm and can be located in local minimum points and on the other hand improving its performance requires a lot of training data. The ANN parameters are optimized using the GA algorithm's proposed controller. Train ANN online to adapt to the system and change the PI-control coefficients without a lot of training data, in addition to avoiding being in the local minimum points.The microgrid tested included various distributed generation units including battery energy storage that tried to create a more realistic frequency response for the microgrid by considering nonlinear factors on the model of these resources. Finally, the simulation results with different perturbations indicate the proper performance of the proposed controller.

Survey of load frequency control strategies in a Microgrid

“…Indeed, within a microgrid, it is important to maintain stable frequency, voltage, and power sharing and also to ensure a gain in terms of cost. Thus, the hierarchical structure is constituted of three levels of control to ensure the above objectives [42,84].…”

“…The ANN approach is applied in [42] in combination with the Genetic Algorithm (GA) to determine the parameters of a PI controller, to control the frequency in an island microgrid. In [43] an adaptive proportional integral (API) controller is replaced by an adaptive neural network (NN) controller to reduce the error signal and cover the frequency uctuations.…”

“…The main advantage is the optimization of controller parameters to make them even more reliable and e cient it is di cult to determine the number of parameters to x at the beginning of the algorithm [42]…”

“…The Sliding Mode Control (SMC) [29][30][31][32][33], ensures a smooth transition. Intelligent strategies such as fuzzy logic (FL) [29,[34][35][36][37][38][39][40][41], arti cial neural network (ANN) [34,42,43], and Arti cial Neuro-Fuzzy Inference System (ANFIS) [44][45][46] are also widely used except that they are highly dependent on data that must be stored to promote their training. Faced with multiple uncertainties in MGs, the H-In nity controller coupled with µ-synthesis and µ-analysis [47][48][49] also allows for improved stability of the frequency.…”

Touchless Input/Output Interface for Device-to-Device Communication

“…These studies are associated with multifunctional devices that can perform tasks such as energy storage, communication, and sensing. An increasing number of studies are exploring the potential of MQW III-nitride diodes. − There are few storage interfaces that achieve great function and low cost. − The development of next-generation memory urgently needs to overcome the constraints of electrical transmission and realize a faster and less interference transmission scheme, and in this regard, the touchless optical I/O interface is a new scheme. , Oh et al reported a double heterojunction nanorod device that can be used for simultaneous light emission and detection. Using a dual-functional perovskite diode, Shan et al proposed a bidirectional optical link between two identical devices.…”