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Transmission network expansion planning problem (TNEP) is of utmost importance in power system now a days. In this paper, a voltage stability indicator (L-index) is applied to limit the voltage of buses. Hence TNEP is formulated incorporating this voltage stability feature. Thus formulated TNEP is termed as voltage stability constrained TNEP (VSC-TNEP). TNEP has been solved using various conventional and non-conventional strategies by various researchers in these days. Here, TNEP and VSC-TNEP for IEEE-24 bus test system is solved using grey wolf optimization technique (GWO) and modified GWO. The modified GWO algorithm namely, fast convergent GWO (FCGWO) is validated over 25 standard benchmark test functions and compared with other techniques.

Transmission network expansion planning (TNEP) problem is a large-scale, complex mixed integer nonlinear programming problem. The solution of TNEP problem is essential to fulfill the load demand in an economical manner. A grey wolf optimization (GWO) algorithm which is a nature-inspired metaheuristic algorithm is used to solve the TNEP problem. Further, a modified GWO is developed, and to validate its result, it is tested on 20 standard benchmark test functions. The basic and modified version of GWO algorithms is applied to solve TNEP problem for Graver’s six-bus and Brazilian 46-bus systems. The obtained results are compared with other state-of-the-art algorithms. The results demonstrate the accuracy as well as proficiency of the proposed algorithm.

This paper present amelioration power quality of hybrid power system integration with PV, wind and battery system by FACTS device. In transmission and distribution system power quality is major restraint. FACTS device have many merits in transmission like flexibility and high speed. Benefit of this system is the diverse sources can be connected to at one junction of power line, because of this these method are ascertaining to be an efficacious and ductile method for the development of the system. Harmonics produce when two diverse source connected to grid and it removed by using UPFC. The unified power flow control is the most promising device of fact family at which study in this paper is concentrated. In the study of this paper UPFC is modeled and interpretation of UPFC in a hybrid-grid connected system is examined. The extraordinary feature of the UPFC is such that it can control flow of real and reactive power as well as voltage amplitude at the same time at the UPFC nodes. The planned model is simulating in MATLAB/Simulink software.

Due to hike in price of conventional source of energy we attentive toward non -conventional source of energy. This paper with present of a hybrid solar-wind energy system with gird connected. Hybrid solar-wind system consist wind, solar source and ac loads. In solar dc output system is raised by boost converter. MPPT technique is used to maximize the output of solar system. The wind subsystem is made of a permanent magnet synchronous generator and rectifier, boost converter for maximum output. Voltage regulator is used for controlling the hybrid solar & wind system. Modeling of hybrid PV and wind turbine which is controlled by voltage regulator is explained. To increase reliability & reduce reliance on one single source we connected these two systems.

The power system is a complex interconnected network which can be subdivided into three components: generation, distribution, and transmission. Capacitors of specific sizes are placed in the distribution network so that losses in transmission and distribution is minimum. But the decision of size and position of capacitors in this network is a complex optimization problem. In this paper, Limacon curve inspired local search strategy (LLS) is proposed and incorporated into spider monkey optimization (SMO) algorithm to deal optimal placement and the sizing problem of capacitors. The proposed strategy is named as Limacon inspired SMO (LSMO) algorithm. In the proposed local search strategy, the Limacon curve equation is modified by incorporating the persistence and social learning components of SMO algorithm. The performance of LSMO is tested over 25 benchmark functions. Further, it is applied to solve optimal capacitor placement and sizing problem in IEEE-14, 30 and 33 test bus systems with the proper allocation of 3 and 5-capacitors. The reported results are compared with a network without a capacitor (un-capacitor) and other existing methods.

The nature-inspired algorithms NIAs have shown efficiency to solve many complex real-world optimisation problems. The efficiency of NIAs is measured by their ability to find adequate results within a reasonable amount of time, rather than an ability to guarantee the optimal solution. This paper presents a solution for lower order system modelling using spider monkey optimisation SMO algorithm to obtain a better approximation for lower order systems and reflects almost original higher order system's characteristics. Further, a local search strategy, namely, power law-based local search is incorporated with SMO. The proposed strategy is named as power law-based local search in SMO PLSMO. The efficiency, accuracy and reliability of the proposed algorithm is tested over 20 well-known benchmark functions. Then, the PLSMO algorithm is applied to solve the lower order system modelling problem.

Transmission network expansion planning problem (TNEP) is extensive, complicated, nonlinear programmable issue. A cost-effective result of TNEP is required to get the power generation pattern with load demand in economical and reliable manner. In this paper, AC optimal power flow-based TNEP is solved using a recent nature-inspired algorithm, namely grey wolf optimization algorithm (GWO). The GWO is tested to this TNEP for solution of six bus Graver’s system and 24 bus IEEE system. The reported outcomes reveal that GWO is cost-effective and more accurate strategy to solve this TNEP.

Low frequency oscillations are the major problem in power system. To magnify the power system stability, new FACTS device is introduced i.e. Distributed Power Flow Controller (DPFC) for damping the low frequency electro-mechanical oscillations. DPFC works on the theory of D-FACTS (distributed FACTS). It is modified form of Unified Power Flow Controller (UPFC) with removal of common DC link in UPFC. In this paper, optimal design problem is solved using Genetic Algorithm (GA) and Differential Evolution (DE) algorithm to optimize the damping controller parameters. Results shows that DPFC employed with DE algorithm improves the stability more effectively compared with GA.

As energy demand is increasing rapidly, our deployment must be tending towards Renewable Energy Sources (RES), in order to meet the demand. Photovoltaic (PV) is one of the most focused RES due to plenty than other sources. Before serving to the load, the generated power by solar array is treated electronically and this process is called 'power conditioning'. In power conditioning, two major functions are frequently discussed i.e. DC-AC conversion and maximum power point tracking (MPPT). In order to supply the maximum power to the load, it is desired to operate the solar array at its maximum power point (MPP). Maximum Power Point Tracker automatically and electronically matches the load with the array. There are various techniques to determine the maximum power point. In this paper, one soft computing technique is discussed for this purpose i.e. Fuzzy Logic. With the fuzzy logic based control technique, the duty cycle of the boost converter is controlled in order to obtain the maximum power from PV array. Fuzzy logic controller (FLC) based MPPT system is implemented to the grid through multilevel inverter (MLI).

Induction motors are widely used in power system due to their robustness, even though induction motors are highly non-linear and their control is quite complicated. Proportional integral derivative acceleration PIDA controller is used with induction motor for improving its efficiency. But, finding optimal PIDA controller parameters is a complex task and cannot be easily dealt by the available analytical strategies. As the nature inspired algorithms NIAs have shown their accuracy and efficiency to solve such complicated optimization problems, in this paper, a recent NIA, namely Spider Monkey Optimization SMO algorithm is used for finding the optimal PIDA controller's parameters for controlling the induction motor. As per the best of authors knowledge, it is the first attempt to design optimal PIDA controller's parameters for induction motor through any swarm intelligence motivated algorithm.

Optimal power flow OPF is the most requisite tool in the power system analysis. The OPF is relatively a difficult constrained optimisation problem and broadly solved by conventional as well as modern intelligent methods. In this paper the authors proposed a Levy flight spider monkey optimisation LFSMO algorithm to solve the standard OPF problem for IEEE 30-bus system, which is proposed to improve the exploitation capability of spider monkey optimisation SMO algorithm. The performance of LFSMO is evaluated by testing it over 25 benchmark functions.