The Static synchronous compensator (STATCOM) is a renowned FACTS (flexible alternating current transmission system) device used in power grids to cope with protean conditions. This article provides a comprehensive bibliographic review of various aspects of STATCOM developed over the last 16 years. The paper includes a detailed study presenting different models, test systems, and results of various researches. The study covers the modelling, control technology, stability, optimal location, applications, and installation of STATCOM. The modelling of STATCOM is diverse and include various models such as voltage source converters, software-based models, load flow models, pulse width modulation, and modular multilevel converters. The control technology includes a PI/PID controller, fuzzy-based, feedback controller, model predictive controller, sliding mode controller, and miscellaneous controller based on different schemes, algorithms, and controllers. In studying the stability analysis, all stability aspects such as steady-state stability, dynamic stability, and transient stability are considered for the stability analysis. The genetic algorithm, heuristic algorithm, probabilistic technique and branch and bound approach are mainly used for the optimal placement of STATCOM as described in the available research paper. Various applications of STATCOM in power system are identified such as optimal power flow, voltage stability, voltage fluctuation mitigation, fault analysis and total harmonic distortion. This paper, also mentions the current installation of STATCOM is also mentioned with their different locations such as China (2018), and Canada (2019). The current functions of STATCOM still have many limitations. So there is still a lot of room for improvement. This paper will help the researcher finding the reference of a particular topic of interest in different aspects of STATCOM. The finding of different sections of the paper can help the researchers to develop new ideas and work on different prospective of the device.

This paper offers three easy-to-use metaphor-less optimization algorithms proposed by Rao to solve the optimal power flow (OPF) problem. Rao algorithms are parameter-less optimization algorithms. As a result, algorithm-specific parameter tuning is not required at all. This quality makes these algorithms simple to use and able to solve various kinds of complex constrained optimization and engineering problems. In this paper, the main aim to solve the OPF problem is to find the optimal values of the control variables in a given electrical network for fuel cost minimization, real power losses minimization, emission cost minimization, voltage profile improvement, and voltage stability enhancement, while all the operating constraints are satisfied. To demonstrate the efficacy of Rao algorithms, these algorithms have been employed in three standard IEEE test systems (30-bus, 57-bus, and 118-bus) to solve the OPF problem. The OPF results of Rao algorithms and the results provided by other swarm intelligence (SI)/evolutionary computing (EC)-based algorithms published in recent literature have been compared. Based on the outcomes, Rao algorithms are found to be robust and superior to their competitors.

A new hybrid meta-heuristic approach Jaya–PPS, which is the combination of the Jaya algorithm and Powell’s Pattern Search method, is proposed in this paper to solve the optimal power flow (OPF) problem for minimization of fuel cost, emission and real power losses and total voltage deviation simultaneously. The recently developed Jaya algorithm has been applied for the exploration of search space, while the excellent local search capability of the PPS (Powell’s Pattern Search) method has been used for exploitation purposes. Integration of the local search procedure into the classical Jaya algorithm was carried out in three different ways, which resulted in three versions, namely, J-PPS1, J-PPS2 and J-PPS3. These three versions of the proposed hybrid Jaya–PPS approach were developed and implemented to solve the OPF problem in the standard IEEE 30-bus and IEEE 57-bus systems integrated with distributed generating units optimizing four objective functions simultaneously and IEEE 118-bus system for fuel cost minimization. The obtained results of the three versions are compared to the Dragonfly Algorithm, Grey Wolf Optimization Algorithm, Jaya Algorithm and already published results using other methods. A comparison of the results clearly demonstrates the superiority of the proposed J–PPS3 algorithm over different algorithms/versions and the reported methods.

Optimal power flow solutions are required for the cost-effective operation and control of an existing power system as well as for its future expansion planning. In this paper, a hybrid meta-heuristic Jaya-Powell’s Pattern Search method is proposed to solve the optimal power flow problem integrated with distributed generating units. Powell’s Pattern Search method has been incorporated into the Jaya algorithm in three different ways, resulting in three variants namely; Jaya-PPS1, Jaya-PPS2, and Jaya-PPS3. Performance of these variants are evaluated by solving optimal power flow in IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus systems. In all the cases, Jaya-PPS1 is found to provide a lower objective function value with a smaller deviation over multiple runs than other algorithms. For example, the combined objective function found by Jaya-PPS1 for IEEE 30-bus system is 937.340, with a reduction of 2.86% of the original system. This benefit increases further with system size.