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Power quality issues highly affect the secure and economic operations of the power system. Although numerous methodologies are reported in the literature, flexible alternating current transmission system (FACTS) devices play a primary role. However, the FACTS devices require optimal location and sizing to perform the power quality enhancement effectively and in a cost efficient manner. This paper aims to attain the maximum power quality improvements in IEEE 30 and IEEE 57 test bus systems.

This paper contributes the adaptive whale optimization algorithm (AWOA) algorithm to solve the power quality issues under deregulated sector, which enhances available transfer capability, maintains voltage stability, minimizes loss and mitigates congestions.

Through the performance analysis, the convergence of the final fitness of AWOA algorithm is 5 per cent better than artificial bee colony (ABC), 3.79 per cent better than genetic algorithm (GA), 2,081 per cent better than particle swarm optimization (PSO) and fire fly (FF) and 2.56 per cent better than whale optimization algorithm (WOA) algorithms at 400 per cent load condition for IEEE 30 test bus system, and the fitness convergence of AWOA algorithm for IEEE 57 test bus system is 4.44, 4.86, 5.49, 7.52 and 9.66 per cent better than FF, ABC, WOA, PSO and GA, respectively.

This paper presents a technique for minimizing the power quality problems using AWOA algorithm. This is the first work to use WOA-based optimization for the power quality improvements.

The purpose of this paper is to enhance the line congestion and to minimize power loss. Transmission line congestion is considered the most acute trouble during the operation of the power system. Therefore, congestion management acts as an effective tool in using the available power without breaking the system hindrances or limitations.

Over the past few years, determining the optimal location and size of the devices have pinched a great deal of consideration. Numerous approaches have been established to mitigate the congestion rate, and this paper aims to enhance the line congestion and minimize power loss by determining the compensation rate and optimal location of a thyristor-switched capacitor (TCSC) using adaptive moth swarm optimization (AMSO) algorithm.

An AMSO algorithm uses the performances of moth flame and the chaotic local search-based shrinking scheme of the bacterial foraging optimization algorithm. The proposed AMSO approach is executed and discussed for the IEEE-30 bus system for determining the optimal location of single TCSC and dual TCSC.

In addition to this, the proposed algorithm is compared with various other existing approaches, and the results thus obtained provide better performances than other techniques.

A system operator (SO) of a transmission network consistently aims to minimise operating costs whilst still maintaining a certain degree of system adequacy. One of the ways to achieve this is by minimising the level of spinning reserve (SR) in the system. In order to do so, the level of SR must be analysed. This study looks at quantifying the risk of inadequacy when the SR is varied. A study was done for a period of 24 h with 30 m intervals to determine the risk level at each period. The number of generators despatched, system power margin and the system sell price was all taken into account. Risk was then computed by factoring the probability of generation inadequacy and the cost of purchasing the imbalance from the balancing market.

The loading and power variations in the power system, especially for the peak hours have abundant concussion on the loading patterns of the open access transmission system. During such unconditional state of loading the transmission line parameters and the line voltages show a substandard profile, which depicts exaction of congestion management of the power line in such events. The purpose of this paper is to present an uncomplicated and economical model for congestion management using flexible AC transmission system (FACTS) devices.

The approach desires a two-step procedure, first by optimal placement of thyristor controlled series capacitor (TCSC) and static VAR compensator (SVC) as FACTS devices in the network; second tuning the control parameters to their optimized values. The optimal location and tuning of TCSC and SVC represents a hectic optimization problem, due to its multi-objective and constrained nature. Hence, a reassuring heuristic optimization algorithm inspired by behavior of cat and firefly is employed to find the optimal placement and tuning of TCSC and SVC.

The effectiveness of the proposed model is tested through simulation on standard IEEE 14-bus system. The proposed approach proves to be better than the earlier existing approaches in the literature.

With the completed simulation and results, it is proved that the proposed scheme has reduced the congestion in line, thereby increasing the voltage stability along with improved loading capability for the congested lines.

The usefulness of the proposed scheme is justified with the computed results, giving convenience for implementation to any practical transmission network.

This paper fulfills an identified need to study exaction of congestion management of the power line.

The purpose of this paper is to optimally locate and size the FACTS device, namely, interline power flow controller in order to minimize the total cost and relieve congestion in a power system. This security analysis helps independent system operator (ISO) to have a better planning and market clearing criteria during any operating state of the system.

A multi-objective optimization problem has been developed including real power performance index (RPPI) and expected security cost (ESC). A security constrained optimal power flow has been developed as expected security cost optimal power flow problem which gives the probabilities of operating the system in all possible pre-contingency and post-contingency states subjected to various equality and inequality constraints. Maximizing social welfare is the objective function considered for normal state, while minimizing compensations for generations rescheduling and maximizing social welfare are the objectives in case of contingency states. The proposed work is viewed as a two level problem wherein the upper-level problem is to optimally locate IPFC using RPPI and the lower-level problem is to minimize the ESC subjected to various system constraints. Both upper-level and lower-level problem are solved using particle swarm optimization and The performance of the proposed algorithm is tested under severe line outages and has been validated using IEEE 30 bus system.

The proposed methodology shows that IPFC controls the power flows in the network without generation rescheduling or topological changes and thus improves the performance of the system. It is found that the benefit achieved in the ESC due to the installation of IPFC is greater than the annual investment cost of the device. ISO cannot achieve minimum total system cost by merely rescheduling generators. Instead of rescheduling, FACTS devices can be used for compensation by achieving minimum cost. IPFC can be used to compensate the congested lines and transfer cheaper power from generators to consumers.

Operational reliability, financial profitability and efficient utilization of the existing transmission system infrastructure has been achieved using single FACTS device. Instead of using multiple FATCS devices, if a single FACTS device like IPFC which itself can compensate several transmission lines is used, then in addition to the facility for independently controlled reactive (series) compensation of each individual line, it provides a capability to directly transfer real power between the compensated lines. Hence an attempt has been made in this paper to incorporate IPFC for relieving congestion in a deregulated environment. However, no previous researches have considered incorporating compensation of multi-transmission line using single IPFC in minimizing ESC. Thus, in this paper, the authors indicate how much the ESC is reduced by installing IPFC.

The main purpose behind this work is to explore the methods already proposed in various literatures to overcome the issues associated with VAr management in a competitive environment. Managing reactive power support service in competitive electricity market environment has become an important constituent of ancillary services. The characteristics of VAr generation/absorption do not allow its transmission over a long distance. The issues associated with the localized nature of reactive power must be considered during the valuation, planning, pricing and allocation of VAr producing/absorbing resources. In this review work, the key issues associated with the reactive support and the techniques used to tackle these issues in various utilities across the globe are been discussed in brief. In the literature, numerous renowned authors propose various methods to manage reactive power with various types of structural and operational scenarios. These methods are also discussed briefly in this paper. The experience with VAr management in some matured electricity market is also discussed in this paper.

Discussion of various issues associated with reactive power management and methods/techniques to overcome these, has been carried out in this paper. The methods were proposed in various literatures related to reactive power management by some of the renowned authors and adopted by various electric utilities.

The review work may be useful for utilities to develop a quick insight on reactive support services to control the voltage profile and also, it may be a useful asset for the researchers working in this area.

The paper is organized with different sections to elaborate the issues and associated methods. This paper is a single piece of work, which addresses reactive power planning, pricing for VAr support, market issues and valuation of VAr utilization.

Optimal placement of static VAR compensator (SVC) devices not only improves the voltage profile (VP) but also reduces the active power loss (APL) and enhances the voltage stability (VS) through injecting appropriate VARs at optimal buses. The traditional mathematical methods may not provide global best solution and pose difficulties in handling multi-objective SVC placement (SVCP) problem with complex constraints and forcefully place all the given number of SVCs in the system without assessing their real requirements in enhancing the chosen performances. The purpose of this paper is to formulate the SVCP as a multi-objective optimization problem and solve it using a metaheuristic algorithm for global best solution.

The proposed SVCP method uses improved harmony search optimization (IHSO) with dissonance-avoiding mechanism for obtaining the global best solution through driving away the solution from the sub-optimal traps. In addition, the method uses a self-adaptive technique for optimally tuning the IHSO parameters and places only the required number of SVCs from the given number of SVCs.

This paper presents the results of the proposed method for 14, 30 and 57 bus systems and exhibits that the proposed method outperforms the existing SVCP methods in achieving the desired performances.

This paper proposes a new self-adaptive IHSO based SVCP method for optimally placing only the required number of SVCs with a goal of attaining the global best performances.

This paper proposes an ANN based adaptive damping control scheme for the unified power flow controller (UPFC) to damp the low frequency electromechanical power oscillations. In this paper a novel damping control strategy based on the time‐domain analysis of system transient energy function (TEF) is proposed and implemented by using well tuned conventional PI controllers to obtain the preliminary training data for the design of the proposed controllers. The multi‐layered feed forward neural network with error back‐propagation training algorithm is employed in this study. Models of UPFC and ANN controllers suitable for incorporating with the transient simulation programs are derived and tested on a revised IEEE nine‐bus test system. Comprehensive simulation results demonstrate the great potential of using UPFC in damping control and the excellent performance of the proposed control scheme.

The aim of the paper is to develop a novel genetic algorithm (GA)‐based supplementary NeuroFuzzy damping control system for the unified power flow controller (UPFC).

The designed scheme employs a micro‐GA (μ‐GA) to avoid being trapped in a local minimum as opposed to the use of the classical back‐propagation technique. The scheme also uses the “Grand‐Parenting” technique for seeding the initial population to hasten the GA convergence speed. To further speed up the GA for solving the optimization problem, a parallel μ‐GA scheme is also used.

It has been discovered that a parallel μ‐GA scheme with three computers setup is approximately three times faster than the μ‐GA with a single computer node. Also when μ‐GA is integrated with the “Grand‐Parenting” technique for seeding the initial population, it would hasten the convergence speed. The control scheme exhibits strong robustness and excellent damping performance when tested on a multi‐machine power system.

Presentation of a novel NeuroFuzzy‐based UPFC that exhibits strong robustness and excellent damping performance.

Forecasting of stock indices is a challenging issue because stock data are dynamic, non-linear and uncertain in nature. Selection of an accurate forecasting model is very much essential to predict the next-day closing prices of the stock indices. The purpose of this paper is to develop an efficient and accurate forecasting model to predict the next-day closing prices of seven stock indices.

A novel strategy called quasi-oppositional symbiotic organisms search-based extreme learning machine (QSOS-ELM) is proposed to forecast the next-day closing prices effectively. Accuracy in the prediction of closing price depends on output weights which are dependent on input weights and biases. This paper mainly deals with the optimal design of input weights and biases of the ELM prediction model using QSOS and SOS optimization algorithms.

Simulation is carried out on seven stock indices, and performance analysis of QSOS-ELM and SOS-ELM prediction models is done by taking various statistical measures such as mean square error, mean absolute percentage error, accuracy and paired sample t-test. Comparative performance analysis reveals that the QSOS-ELM model outperforms the SOS-ELM model in predicting the next-day closing prices more accurately for all the seven stock indices under study.

The QSOS-ELM prediction model and SOS-ELM are developed for the first time to predict the next-day closing prices of various stock indices. The paired t-test is also carried out for the first time in literature to hypothetically prove that there is a zero mean difference between the predicted and actual closing prices.