Intelligent photovoltaic farms for robust frequency stabilization in multi-area interconnected power system based on PSO-based optimal Sugeno fuzzy logic control

Abstract

a b s t r a c tCurrently, the grid-connected large PV farms are extensively installed in power systems. Nevertheless, in addition to the load change, the intermittent power output of PV farms may lead to the serious problem of the system frequency fluctuation. To handle this problem, this paper proposes a new design of Sugeno fuzzy logic controller based on particle swarm optimization (PSO-SFLC) of intelligent PV farms for the frequency stabilization in a multi-area interconnected power system. To handle various scenarios, the frequency deviations and solar insolations are used as input signals of the PSO-SFLC. The output signal of the PSO-SFLC is a command signal for adjusting PV output power. The output power of PV is controlled by the PSO-SFLC to meet the load demand so that the system frequency fluctuation can be suppressed. Without the difficulty of trial and error, the optimal input and output membership functions, and control rules of PSO-SFLC are automatically achieved by PSO. Simulation study in a three-area loop interconnected power system with large PV farms elucidates that the frequency stabilizing performance and robustness of the PV equipped with the PSO-SFLC is much superior to that of the PV with the SFLC and the PV with the maximum power point tracking control in scenarios with various solar insolations and loading conditions.

Use of the Genetic Algorithm-Based Fuzzy Logic Controller for Load-Frequency Control in a Two Area Interconnected Power System

“…A MATLAB-Simulink model of the two area interconnected renewable energy power system is shown in Figure 6, where, ∆ is the frequency of the system, is the regulation constant, ∆ is the load variations, ∆ is the power variation depending on the sunlight of the PV cell, and is the field controller output. The whole system with multi variable parameters is described in terms of Equations (9)- (12). Parameters of two-area power system with PV-SPP are shown in Appendix A.…”

“…The whole system with multi variable parameters is described in terms of Equations (9)- (12). Parameters of two-area power system with PV-SPP are shown in Appendix A.…”

“…Khalghani et al compared an emotional controller with the conventional PID controller [11]. A sugeno fuzzy logic controller (S-FLC), optimized by particle swarm optimisation (PSO), was applied by Sangawong et al [12]. For micro grid networks with photovoltaic (PV) systems, battery units, micro wind turbines (MWT), and fuel cells, the most suitable control method is a self-adjusting control method [11,13,14].…”

A Polar Fuzzy Control Scheme for Hybrid Power System Using Vehicle-To-Grid Technique

“…Recently, there have been some studies dedicated to frequency control approaches for the hybrid power system using FLC [17][18][19][20][21], µ synthesis scheme [22], H ∞ and µ-synthesis approach [23], neuro-fuzzy control [24], FLC with the particle swarm optimization (PSO) algorithm implementation [25,26], FLC with chaotic PSO [27], PSO with mixed H 2 /H ∞ control [28], the quasi-oppositional harmony search algorithm (QOHSA) [29], sliding mode control (SMC) [30], multiple model predictive control (MMPC) [31], multi-variable generalized predictive control (MGPC) [32] and Type-2 FLC with the modified harmony search algorithm (MHSA) [33] with promising results. Despite this, this research tries to mitigate supply error and modify system frequency in the face of renewable suppliers' uncertainties and random load fluctuations to face the deficits associated with most of these previous techniques, such as H ∞ and FLC schemes.…”

Accelerating particle swarm optimization using crisscross search

“…As the PSO algorithm is simple in concept, easy to implement and computationally inexpensive, it attracts the attention of many scholars and researchers in the last two decades. PSO has now been successfully applied to a wide range of application areas such as electric power systems [2,5,8,10,25,29,51,53,70], engineering design [27,30,32,67,76,81], neural networks [1,6,16,24,43,60,65,71,74] and so on.…”