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This paper aims to select the optimal location and setting parameters of Static VAR Compensator (SVC) controller using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) to mitigate small signal oscillations in a multimachine power system. Though Power System Stabilizers (PSSs) are prime choice in this issue, its performance gets affected by changes in network configurations, load variations etc. Hence installation of FACTS device, SVC has been suggested here in order to achieve appreciable damping of oscillations. However, performance of any FACTS devices highly depends upon its parameters and suitable location in the power network. In this paper PSO as well as GA based techniques are used to investigate this problem. An attempt has also been made to compare the performance of the PSO based SVC controller with its GA based design. The validity of the proposed techniques is simulated in a multimachine system following two common disturbances. It has been revealed that the PSO based SVC controller is more effective than GA based controller even during critical loading condition. [ABSTRACT FROM PUBLISHER]

This paper focuses on unit sizing of stand-alone hybrid energy systems using bees algorithm. With different combination of renewable sources, two types of hybrid energy systems are taken as test systems. Apart from the non-conventional sources like wind turbines, solar panels along with micro hydro plant, the systems have been optimized with fuel cell, converters, electrolyzers and hydrogen tanks. Different performance parameters such as net present cost (NPC), cost of energy (COE) and generation cost (GC) of power production have been considered. Out of the two systems studied, it is seen that the combination of hydro-wind-fuel cell is the most feasible hybrid energy system in respect to their net present cost and cost of energy. Again it is seen that, the bees algorithm is quite efficient in providing a global solution and makes the choice more easier. [ABSTRACT FROM PUBLISHER]

This paper proposes a Local Linear Wavelet Neural Network (LLWNN) — a combination of Artificial Neural Network and Local Linear Wavelet Technique-to predict electricity prices for one hour to twenty four hours in advance. The prices of Ontario electricity market are taken as experimental data. Multilayer Perceptron (MLP) model has also been discussed for comparison purpose. Backpropagation learning algorithm is used to train both the models. Further to get more accuracy, both the models have been integrated with Particle Swarm Optimization (PSO). The Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) are used to find out the forecasting performance of the proposed model. [ABSTRACT FROM PUBLISHER]

The paper presents a multi-objective planning approach for electrical distribution systems incorporating shunt capacitor banks. In this planning, the number of feeders, feeder routes, and number, locations, and rating of shunt capacitor banks for a distribution system are determined using a multi-objective optimization approach. The objectives considered for this optimization are: (i) total investment cost minimization, (ii) network reliability maximization, (iii) minimization of network power loss, and (iv) minimization of node voltage deviation. The last three objectives are aggregated into a single objective, named as network performance metric. This objective and the first objective are simultaneously optimized to obtain a set of non-dominated solutions. The solution algorithm is Multi-objective particle swarm optimization (MOPSO). Three different MOPSO variants are used and their performances are compared. The result of this planning approach is compared with that of the planning without capacitor banks. The proposed planning approach is validated on a 54-node distribution system planning problem. [ABSTRACT FROM PUBLISHER]

The aim of this paper is the tuning of a PI controller using PSO techniques for autonomous hybrid energy generation/energy storage system. The autonomous hybrid generation system consisting of wind turbine generators (WTG), solar photovoltaic (PV), diesel engine generators (DEG), fuel cells (FC), battery energy storage system (BESS), ultra capacitors ( UC) and aqua electrolyzer (AE) has been considered for simulation studies. The power system frequency deviates for sudden changes in load or generation or the both. The comparative performance of the controllers installed to alleviate this frequency deviation for different hybrid systems, is carried out using time domain simulation. In practice, PI controller is tuned manually which is difficult and time consuming. The computational intelligence has opened paths to a new generation of advanced process control. Here, PSO is used for optimization of controller gains of the proposed hybrid system. The simulation results demonstrate the effectiveness of the PSO based controller in terms of reduced settling time, overshoot and oscillations. The results are compared with conventional controller. [ABSTRACT FROM PUBLISHER]

A nonlinear active noise control (ANC) system based on a couple of low complexity nonlinear networks are developed in this paper. These are the evolutionary computing based feed forward nonlinear network (FFNN) and the evolutionary computing based feed forward recursive nonlinear network (FFRNN). The new method does not require the identification of the secondary path, which not only improves the stability of the ANC system but also reduces the computational complexity. The design of the proposed ANC systems is viewed as a single objective optimization problem in which the weights of the ANC system are updated using particle swarm optimization (PSO) based evolutionary algorithm. [ABSTRACT FROM PUBLISHER]

Power system structure is undergoing through restructuring process since a decade. Everyday a bulk amount of power is generated, transmitted and distributed via transmission network. The active power or the real power generated from the generator needs the reactive power for supporting its own transmission. This reactive power generation has some minimum limit which if generator fails to produce it has to take support from system operator for smooth real power flow in worth of paying money. While the generator produces excess reactive power it can expect remuneration from the system operator. In deregulated power system minimal reactive power generation is considered as an important issue as cost is related to it. In this paper IEEE 14-bus test system is considered for solving the problem. As a solving tool a well known population based metaheuristics technique: particle swarm optimisation is chosen which tries to optimize the problem by controlling the generator bus voltage. The choice of the objective function and the mentioned optimisation technique is considered by through literature survey. [ABSTRACT FROM PUBLISHER]

The main objective of this paper is to accurately estimate the fault location in a series compensated transmission line using integration of discrete wavelet transform, particle swarm optimization (PSO) and support vector machine based algorithm. Estimation of accurate fault location will lead to quicker restoration of the supply. Instantaneous values of faulty current, voltage and power signals are available at the relay location. Using 10-level Discrete Wavelet Transform (DWT), the available signals are decomposed and thereafter the statistical features are obtained from the decomposed signals. Forward feature selection algorithm is used to select the best feature set. Selected features are then applied to the support vector machine (SVM) for estimating the fault distance. PSO algorithm is used to select the best SVM parameter by global searching technique. Proposed fault locator has been trained and tested for different fault scenarios (fault resistance and phase difference). The test results demonstrate that the adopted technique is a reliable method for estimating fault locations accurately. [ABSTRACT FROM PUBLISHER]