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Consideration of transient stability constraints in optimal power flow (OPF) problems is increasingly important because modern power systems tend to operate closer to stability boundaries due to the rapid increase of electricity demand and the deregulation of electricity markets. Transient stability constrained OPF (TSCOPF) is however a nonlinear optimization problem with both algebraic and differential equations, which is difficult to be solved even for small power systems. This paper develops a robust and efficient method for solving TSCOPF problems based on differential evolution (DE), which is a new branch of evolutionary algorithms with strong ability in searching global optimal solutions of highly nonlinear and nonconvex problems. Due to the flexible properties of DE mechanism, the hybrid method for transient stability assessment, which combines time-domain simulation and transient energy function method, can be employed in DE so that the detailed dynamic models of the system can be incorporated. To reduce the computational burden, several strategies are proposed for the initialization, assessment and selection of solution individuals in evolution process of DE. Numerical tests on the WSCC three-generator, nine-bus system and New England ten-generator, 39-bus system have demonstrated the robustness and effectiveness of the proposed approach. Finally, in order to deal with the large-scale system and speed up the computation, DE is parallelized and implemented on a Beowulf PC-cluster. The effectiveness of the parallel DE approach is demonstrated by simulations on the 17-generator, 162-bus system. [ABSTRACT FROM AUTHOR]

Transient stability preventive control (TSPC) ensures that power systems have a sufficient stability margin by adjusting power flow before faults occur. The generation of TSPC measures requires accuracy and efficiency. In this paper, a novel model interpretation-based multi-fault coordinated data-driven preventive control optimization strategy is proposed. First, an augmented dataset covering the fault information is constructed, enabling the transient stability assessment (TSA) model to discriminate the system stability under different fault scenarios. Then, the adaptive synthetic sampling (ADASYN) method is implemented to deal with the imbalanced instances of power systems. Next, an instance-based machine model interpretation tool, Shapley additive explanations (SHAP), is embedded to explain the TSA model's predictions and to find out the most effective control objects, thus narrowing the number of control objects. Finally, differential evolution is deployed to optimize the generation of TSPC measures, taking into account the security and economy of TSPC. The proposed method's efficiency and robustness are verified on the New England 39-bus system and the IEEE 54-machine 118-bus system. [ABSTRACT FROM AUTHOR]

• A novel controller based on the combination of a FOPID controller, a TID controller, and a 2DOF controller is proposed to rectify the ACE signal and consequently to improve the frequency response of multi-area power systems. • The AVR loop along with PSS is added to the LFC model to complete the LFC aim and considering the cross‐coupling of LFC with AVR. • A modified SCA (MSCA) is used to obtain the best controllers gains during disturbances. • The simulation studies are conducted on different power systems. • Sensitivity analysis is done to exhibit the robustness of the proposed LFC scheme against variations of the grid parameters. This paper presents a novel load frequency control (LFC) model for an interconnected thermal two-area power system in the presence of wind turbine generation and redox flow battery (RFB). The study model includes frequency and voltage excitation loops with needed interactions between them along with the power system stabilizer. A two-degree of freedom (2DOF)-based controller called 2DOF-Hybrid controller is developed as secondary controller in automatic generation control (AGC) to adjust the power outputs of generator and RFB. Also, the dynamic performance of the proposed controller in the RFB loop is evaluated. The Hybrid controller comprises a fractional-order proportional-integral-derivative (FOPID) controller and a tilt-integral-derivative (TID) controller. In order to obtain accurate and realistic results, the outputs of thermal power plants are restricted by considering the limitations of the governor dead-band and generation rate constraint. Since the controller performance depends on its parameters, these parameters are optimized using a modified sine–cosine algorithm (MSCA). The dynamic performance of the proposed 2DOF-Hybrid controller as secondary controller of the AGC loop is compared with integral-double-derivative (IDD), integral-tilt-derivative, proportion-integral–derivative (PID)-DD, 2DOF-PID, 2DOF-TID, and 2DOF-FOPID ones under different scenarios. In addition, the superiority of the MSCA is compared with benchmark metaheuristic methods including an SCA, a genetic algorithm, a particle swarm optimization, and a differential evolution. The sensitivity analysis is also carried out to show the robustness of the proposed controller versus the changes of the parameters. The simulation studies on two-area and New England 39-bus power systems are carried out to examine the advantage of the presented LFC scheme. A range of power system signals such as frequencies of areas, terminal voltages, and tie-line power flow is demonstrated to compare the controllers. The results disclose that the proposed LFC scheme provides better dynamic performance compared to other ones. Moreover, RFB modeling based on the proposed controller is superior to conventional RFB modeling in reducing the amplitude of the oscillations. [ABSTRACT FROM AUTHOR]

This article presents a new algorithm aimed toward effective handling of the transient-stability-constrained optimal power flow (TSC_OPF) problem. The algorithm is a hybridized version of the existing differential evolution (DE) and symbiotic organism search (SOS) algorithms. It combines exploration and exploitation ability of both algorithms which results in its better performance as compared to DE and SOS acting alone. It was tested on IEEE 30 bus test system and the New England 39 bus test system. The results obtained by the proposed approach were compared with conventional TSC_OPF and also with other algorithms available in the literature. Results obtained using the proposed approach demonstrates superiority in comparison with other available algorithms in the literature. [ABSTRACT FROM AUTHOR]

This article focuses on the implementation of a hybrid firefly algorithm-particle swarm optimization (FAPSO) scheme for optimizing the parameters of an interval type-2 fractional order fuzzy proportional integral derivative (IT2FOFPID)-based power system stabilizer (PSS) to minimize the low-frequency oscillations in a power system. Here, the IT2FOFPID-based PSS is designed by considering speed deviation and acceleration as input signals. In this article, a single machine infinite bus system and the New England 10 machine 39-bus power system are used for testing and comparing the approaches. Stability studies are also performed using OPAL-RT's OP5600, a real-time digital simulator. The comparative studies demonstrate that the hybrid FAPSO optimized IT2FOFPID-PSS provides better damping and stability performance when compared with the PSSs based on the FA/PSO/ hybrid genetic algorithm and bacterial foraging optimization and hybrid differential evolution and pattern search optimized IT2FOFPID approaches under various operating scenarios. [ABSTRACT FROM AUTHOR]