Small-signal stability analysis of hybrid power system with quasi-oppositional sine cosine algorithm optimized fractional order PID controller

This article deals with the frequency instability problem of a hybrid energy power system (HEPS) coordinated with reheat thermal power plant. A stochastic optimization method called a sine-cosine algorithm (SCA) is, initially, applied for optimum tuning of fractional-order proportional-integral-derivative (FOPI-D) controller gains to balance the power generation and load profile. To accelerate the convergence mobility and escape the solutions from the local optimal level, quasi-oppositional based learning (Q-OBL) is integrated with SCA, which results in QOSCA. In this work, the PID-controller's derivative term is placed in the feedback path to avoid the set-point kick problem. A comparative assessment of the energy-storing devices is shown for analyzing the performances of the same in HEPS. The qualitative and quantitative evaluation of the results shows the best performance with the proposed QOSCA: FOPI-D controller compared to SCA-, grey wolf optimizer (GWO), and hyper-spherical search (HSS) optimized FOPI-D controller. It is also seen from the results that the proposed QOSCA: FOPI-D controller has satisfactory disturbance rejection ability and shows robust performance against parametric uncertainties and random load perturbation. The efficacy of the designed controller is confirmed by considering generation rate constraint, governor dead-band, and boiler dynamics effects

Quasi-oppositional differential search algorithm applied to load frequency control

AbstractIn this article, quasi-oppositional differential search algorithm (QODSA) is proposed for finding an optimal and effective solution for load frequency control (LFC) problem in the power system. Initially, original DSA is employed for fine-tuning of the secondary controller of LFC system and then, quasi-oppositional based learning (Q-OBL) mechanism is integrated into the original DSA to enhance the convergence speed and to find a better solution of LFC problem. To validate the effectiveness of proposed QODSA, four widely used interconnected power system networks are designed and analyzed. The superiority of the proposed method is established by an extensive comparative analysis with other existing evolutionary algorithm’s (EA) using transient analysis method. A critical investigation of simulation results reveals that the proposed QODSA gives simple and better solution compared to original DSA and other reported algorithms. To study the robustness of QODSA, two different random load patterns are projected and results confirm the robustness of the designed controllers. To add some degree of nonlinearity, generation rate constraint and governor dead band effects are considered and their consequence on the system dynamics has been examined. Finally, sensitivity analysis is performed with a wide variation of system parameters

Application of backtracking search algorithm in load frequency control of multi-area interconnected power system

This paper introduces a new powerful evolutionary algorithm called backtracking search algorithm (BSA) for solving load frequency control (LFC) problem in power system. Initially, two-area non-reheat thermal power plant is considered and gains of PI/PID controllers are optimized using BSA. This paper compares BSA’s effectiveness in solving LFC problem with the performances of other optimization techniques reported in the literature. Nonlinearities of power system such as reheater, governor dead band, boiler dynamics and generation rate constraint are included in the system modeling to identify the system stability and its performance is compared with craziness based PSO technique. Additionally, two more test systems namely three-area and four-area hydro-thermal plant with nonlinearity are considered to demonstrate the efficiency of proposed algorithm. The comparative analysis of the performances indicates that the proposed controller gives better results than other techniques available in the literature. Sensitivity analysis showed robustness of proposed controller under loading and parameter uncertainty