Frequency control of the islanded microgrid based on optimised model predictive control by PSO

Abstract

In this paper, the amount of microgrid frequency deviation in the dynamic state can be reduced by improving the frequency controller and implementing a new method. The proposed controller is designed for a microgrid including renewable resources, and the proposed control strategy is such that the controller coefficients are adjusted and optimised at all times by the model predictive control (MPC). The weight parameters of the MPC controller have been optimised by the particle swarm optimisation (PSO) algorithm. The proposed controller is located in the secondary frequency control loop, and by applying a control signal to the sources, the frequency perturbations following the power changes in the microgrid are reduced. The simulation results show that the proposed controller performs better than the Ziegler-Nichols PI controller (PI-ZN) method, PI-based controllers that rely on fuzzy logic (PI-Fuzzy), the fractional-order proportional-integral-derivative (FOPID) controller that is based on chaos particle swarm optimisation (FOPID-CPSO) algorithm and the PID controllers based on CPSO algorithm (PID-CPSO). It has been able to effectively reduce the frequency fluctuations in terms of amplitude and number of oscillations is also more resistant to the uncertainty of microgrid parameters and shows better performance when changing parameters than other methods.

Improving the Power Quality of Island Microgrid With Voltage and Frequency Control Based on a Hybrid Genetic Algorithm and PSO

“…But verifying if this protocol is useful for a large charge amount of load must be made soon and try to be in connection with this work. Frequency deviation (Hz) PSO-GA proposed controller MPC-PSO controller [35] IMPC controller [36] This article has been accepted for publication in IEEE Access. This is the author's version which has not been fully edited and content may change prior to final publication.…”

“…Which shows the integral amount of microgrid frequency deviation from its nominal amount (50 Hz) so that the lower the value, the better the controller performance. Figure 20 shows the changes in the functional index defined in Equation ( 16) for the proposed controller, IMPC [36], and MPC-PSO [35]. As it is known, the proposed controller has the lowest amount of functional index changes, which indicates its proper performance.…”

“…Finally, Figure 21 shows the curve of microgrid frequency changes according to the disturbances presented in Figure 18 and Figure 19. As you can see, the proposed controller has a more suitable response both in terms of reducing overshoot of frequency changes and in terms of reducing frequency settling time, compared to the two MPC-PSO [35] and IMPC [36] controllers.…”

“…In this section, the performance of the proposed controller has been evaluated for uncertainties in load changes, the presence of different DGs, and changes in the generation power of the units, and its results have been compared with other methods. The studied microgrid information is presented in reference [35]. In this microgrid, there are different types of DGs, including a photovoltaic (PV), and a wind turbine generator (WTG).…”

“…As it is known, the proposed controller has the lowest amount of functional index changes, which indicates its proper performance. In reference [35], to improve the PI controller in the microgrid, model predictive control (MPC) is used to optimize the coefficients of the PI controller, and since its purpose is to control the frequency of the microgrid, the performance speed of MPC will be very important. To solve this problem, the PSO algorithm is used to improve the MPC controller both in terms of performance speed and accuracy.…”