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

Optimal allocation of SVC and TCSC using quasi-oppositional chemical reaction optimization for solving multi-objective ORPD problem

This paper presents an efficient quasi-oppositional chemical reaction optimization (QOCRO) technique to find the feasible optimal solution of the multi objective optimal reactive power dispatch (RPD) problem with flexible AC transmission system (FACTS) device. The quasi-oppositional based learning (QOBL) is incorporated in conventional chemical reaction optimization (CRO), to improve the solution quality and the convergence speed. To check the superiority of the proposed method, it is applied on IEEE 14-bus and 30-bus systems and the simulation results of the proposed approach are compared to those reported in the literature. The computational results reveal that the proposed algorithm has excellent convergence characteristics and is superior to other multi objective optimization algorithms. Keywords: Quasi-oppositional chemical reaction optimization (QOCRO), Reactive power dispatch (RPD), TCSC, SVC, Multi-objective optimizatio

Krill herd algorithm applied to short-term hydrothermal scheduling problem

In this paper, krill herd algorithm (KHA) technique is employed to solve the short-term hydrothermal scheduling (HTS) problem. In this article, the potentialities of DE are used in KHA technique to improve the convergence speed and robustness. The practical short-term HTS problem is solved here using KHA technique in which the crossover and mutation operation of differential evolution algorithm (DEA) is employed to efficiently control the local and global search, so that premature convergence may be avoided and global solutions can be achieved. The quality and usefulness of the proposed algorithm is demonstrated through its application to two standard test systems. The simulation results reveal that the current proposal is better in comparison with the other existing techniques in terms of computational time and the quality of the solutions obtained

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

Oppositional based grey wolf optimization algorithm for economic dispatch problem of power system

This article proposes an efficient meta-heuristic approach, namely, oppositional grey wolf optimization (OGWO) algorithm for resolving the optimal operating strategy of economic load dispatch (ELD) problem. The proposed algorithm combines two basic concepts. Firstly, the hunting behavior and social hierarchy of grey wolves are used to search optimal solutions and secondly, oppositional concept is integrated with the grey wolf optimization (GWO) algorithm to accelerate the convergence rate of the conventional GWO algorithm. To show the performance of the proposed algorithm, it is applied on small, medium and large scale test systems for solving ELD problems of 13-unit, 40-unit and 160-unit systems. Comparative studies are carried out to scrutinize the efficiency of the proposed OGWO approach over the conventional GWO and other approaches available in the literature. The simulation results clearly suggest that the proposed OGWO approach is capable of finding better solutions in terms of computational time and fuel cost than the other techniques. Keywords: Economic load dispatch, Evolutionary algorithm, Grey wolf optimization, Oppositional based learning, Power syste

Optimal location of STATCOM using chemical reaction optimization for reactive power dispatch problem

Optimal reactive power dispatch (ORPD) problem has a significant influence on optimal operation of power systems. However, getting optimal solution of ORPD problem is a strenuous task for the researchers. The inclusion of flexible AC transmission system (FACTS) devices in the power system network for solving ORPD problem adds to its complexity. This paper presents the application of chemical reaction optimization (CRO) for optimal allocation of a static synchronous compensator (STATCOM) to minimize the transmission loss, improve the voltage profile and voltage stability in a power system. The proposed approach is carried out on IEEE 30-bus and IEEE 57-bus test systems and the simulation results are presented to validate the effectiveness of the proposed method. The results show that the proposed approach can converge to the optimum solution and obtains better solutions as compared to other methods reported in the literature

Application of Empirical Bode Analysis for Delay-Margin Evaluation of Fractional-Order PI Controller in a Renewable Distributed Hybrid System

For an uninterrupted power supply, renewable energy promises to be a suitable alternative compared to the conventional sources. System delays or communication delays may cause significant synchronization imbalances between various components in big electrical grids. Since the properties of solar and wind generation constantly change with climatic circumstances, engineers encounter many difficulties when substituting sustainable power with conventional electricity. The computation delay margin may be leveraged to handle a time-delayed automatic generation control (AGC) system. In order to regulate a distributed hybrid renewable energy system in a three-area AGC configuration, this paper investigates the influence of the fractional integral order on the stable system’s delay parameter region. By changing the fractional order range, the delay margin can be increased, potentially broadening the time-delayed system’s stability region. The controller’s stability region has dependency on the order of fraction and the time delay. For this purpose, the asymptotic Bode diagram of the time-delayed fractional proportional-integral controller is determined. The gain and phase margins are used to calculate the delay margin for the application in discussion. The Honey Badger algorithm helps to adjust the controller parameters. It is also confirmed that the suggested controller is resilient to random load perturbations, nonlinearities, and parameter variations