Load Frequency Control Using Hybrid Intelligent Optimization Technique for Multi-Source Power Systems

Abstract

The automatic load frequency control for multi-area power systems has been a challenging task for power system engineers. The complexity of this task further increases with the incorporation of multiple sources of power generation. For multi-source power system, this paper presents a new heuristic-based hybrid optimization technique to achieve the objective of automatic load frequency control. In particular, the proposed optimization technique regulates the frequency deviation and the tie-line power in multi-source power system. The proposed optimization technique uses the main features of three different optimization techniques, namely, the Firefly Algorithm (FA), the Particle Swarm Optimization (PSO), and the Gravitational Search Algorithm (GSA). The proposed algorithm was used to tune the parameters of a Proportional Integral Derivative (PID) controller to achieve the automatic load frequency control of the multi-source power system. The integral time absolute error was used as the objective function. Moreover, the controller was also tuned to ensure that the tie-line power and the frequency of the multi-source power system were within the acceptable limits. A two-area power system was designed using MATLAB-Simulink tool, consisting of three types of power sources, viz., thermal power plant, hydro power plant, and gas-turbine power plant. The overall efficacy of the proposed algorithm was tested for two different case studies. In the first case study, both the areas were subjected to a load increment of 0.01 p.u. In the second case, the two areas were subjected to different load increments of 0.03 p.u and 0.02 p.u, respectively. Furthermore, the settling time and the peak overshoot were considered to measure the effect on the frequency deviation and on the tie-line response. For the first case study, the settling times for the frequency deviation in area-1, the frequency deviation in area-2, and the tie-line power flow were 8.5 s, 5.5 s, and 3.0 s, respectively. In comparison, these values were 8.7 s, 6.1 s, and 5.5 s, using PSO; 8.7 s, 7.2 s, and 6.5 s, using FA; and 9.0 s, 8.0 s, and 11.0 s using GSA. Similarly, for case study II, these values were: 5.5 s, 5.6 s, and 5.1 s, using the proposed algorithm; 6.2 s, 6.3 s, and 5.3 s, using PSO; 7.0 s, 6.5 s, and 10.0 s, using FA; and 8.5 s, 7.5 s, and 12.0 s, using GSA. Thus, the proposed algorithm performed better than the other techniques.

Optimization of rwanda power system protection in power blackouts and cascading events

“…The summarized frequency responses of the simulation tests conducted in this research were discussed and compared to the related recent research outputs as described in [14][15][16][17], and [18]. The observed results showed the sensitivity of the developed control system by 99.86% 3 illustrates the smallest frequency characteristics of 0.000015 Hz, 0.0000012 Hz, and 0.251 s; 0.13 Hz, 0 Hz, and 0.251 s for overshoot, frequency steady-state error, and settling time were successfully observed and saved the 75 Megawatts loss respectively by weighing with the related research findings.…”

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“…where K pr , and K i , are the controller parameters. In this paper, we consider the objective function as an integral time absolute error (ITAE) [23]. The ITAE cost function is as given by Equation ( 2) below.…”

“…A comparative analysis of several soft computing techniques for the LFC have been studied in [21] by Gupta et al Further, for a power system with interconnected areas, load frequency control analysis has been carried out in [22] using a hybrid adaptive gravitational search and pattern search algorithm. Recently, Gupta et al reported novel hybrid optimization techniques for addressing the issues of LFC in MAPS comprising multiple sources [23]. Koley et al [24] presented the issue of LFC by considering a power system involving hybrid power plants such as thermal, wind, and photovoltaic generation stations.…”

Closed-Loop Tuning of Cascade Controller for Load Frequency Control of Multi-Area Distributed Generation Resources Optimized by ASOS Algorithm

“…The essential features of LFC are (i) to abolish the frequency error, (ii) to retain the steady power flow over transmission lines, and (iii) to uphold synchronization between the associated generators. The foremost concern of today's power system is to deliver eminence power against incessant load fluctuation, swift mounting load demand, interconnected large power networks, and dispersion of renewable energy sources [1][2]. From time to time, the foremost power production by thermal and hydro sources is not appropriate for  ISSN: 2089-3272 IJEEI, Vol.…”