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In this paper, using particle swarm optimization (PSO) based method is developed to determine the optimal allocation of distributed generators (DGS) on a multi phased unbalanced distribution network. PSO algorithm has been programmed in MATLAB using open source software called OpenDSS in a co-simulation environment to solve the unbalanced three-phase optimal power flow (TOPF) and to find the optimal location and sizing of different types of distributed generators. Using the IEEE 123 node distribution feeder as a test bed, results from the proposed method is compared to those from the repeated load flow (RLF) method. For a realistic study, mixes of all type of DGs are considered. Results indicate that integrating optimally sized DGs at the optimal locations not only reduces the total power loss in the distributed system but improves the voltage profile as well.

Metaheuristics algorithms are widely recognized as one of most practical approaches for combinatorial optimization problems. One the most interesting areas of application are the power systems. In particular, Distribution Systems planning and operation. This paper presents some metaheuristics approaches tosolve a typical combinatorial optimization problem: the Phase Balancing in Low Voltage Electric Distribution Systems. A model supported in Linear Integer-Mixed Programming is presented, to observe and discussing its limitations. From this, is introduced a new metaheuristic, called Fuzzy EvolutionaryParticle Swarm Optimization, based in the Swarm Intelligence Principles and Evolution Strategies, which is extended to fuzzy domain to modeling a multi-objective optimization, by mean of a fuzzy fitness function.A simulation on a real system is presented, and advantages of this approach respect to the Classical Simulated Annealing and Particle Swarm metaheuristics, selected between the most representatives, areevidenced. Fil: Schweickardt, Gustavo Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional; Argentina Fil: Gimenez Alvarez, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan; Argentina Fil: Casanova Pietroboni, Carlos Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional; Argentina

This paper proposes differential search (DS) algorithm to solve distribution system reliability optimization problem. Optimum number and location of remote control switch (RCS) have been found, in order to enhance reliability of a radial distribution system. A multi-objective formulation has been considered with a view to enhance reliability at a compromised cost. DS algorithm utilizes the Brownian-like random-walk movement used by an organism to migrate. Simulation results obtained by DS algorithm have been compared with that of particle swarm optimization (PSO), differential evolutionary algorithm (DE), genetic algorithm (GA), ant colony optimization (ACO) and gravitational search algorithm (GSA). Results show that DS algorithm provides considerably superior performance, in terms of quality of solution obtained and computational efficiency.

A stochastic model for day-ahead Micro-Grid (MG) management is proposed in this paper. The presented model uses probabilistic reconfiguration and Unit Commitment (UC) simultaneously to achieve the optimal set points of the MG’s units besides the MG optimal topology for day-ahead power market. The proposed operation method is employed to maximize MG’s benefit considering load demand and wind power generation uncertainty. MG’s day-ahead benefit is considered as the Objective Function (OF) and Particle Swarm Optimization (PSO) algorithm is used to solve the problem. For modeling uncertainties, some scenarios are generated according to Monte Carlo Simulation (MCS), and MG optimal operation is analyzed under these scenarios. The case study is a typical 10-bus MG, including Wind Turbine (WT), battery, Micro-Turbines (MTs), vital and non-vital loads. This MG is connected to the upstream network in one bus. Finally, the optimal set points of dispatchable units and best topology of MG are determined by scenario aggregation, and these amounts are proposed for the day-ahead operation. In fact, the proposed model is able to minimize the undesirable impact of uncertainties on MG’s benefit by creating different scenarios.

This paper proposes the automatic generation control (AGC) of an interconnected multi-area multi-source hydrothermal power system under deregulated environment. The two equal control areas with hydro and thermal generating power sources are interconnected via AC/DC parallel links. The optimal proportional integral (PI) regulators are designed for the proposed power system to simulate all power market transactions which are possible in a restructured power system. The concept of DISCO participation matrix (DPM) is harnessed to simulate the transactions. Eigenvalue study is conducted to assess the effect of AC/DC parallel links on system performance. The study is also conducted, considering appropriate generation rate constraints (GRCs) for thermal and hydro generating sources. Further, the dynamic responses of the proposed multi-source hydrothermal power system are compared with single-source thermal–thermal power system and it has been ascertained that the responses of proposed power system are sluggish with large overshoots and settling times. Finally, the study is extended to frame and implement optimal PI regulators for the first time for the AGC of a conventional two-area non-reheat thermal power system with governor dead-band nonlinearity. The superiority of the optimal PI regulators has been established by comparing the results with recently published best claimed craziness based particle swarm optimization (CRAZYPSO) and hybrid bacterial foraging optimization algorithm-particle swarm optimization (hBFOA-PSO) algorithms based PI controller tuned for the same interconnected power system.

In this paper, a hybrid combination of Neuro and Fuzzy is proposed as a controller to solve the Automatic Generation Control (AGC) problem in a restructured power system that operates under deregulation pedestal on the bilateral policy. In each control area, the effects of the possible contracts are treated as a set of new input signal in a modified traditional dynamical model. The prominent advantage of this strategy is its high insensitivity to large load changes and disturbances in the presence of plant parameter discrepancy and system nonlinearities. This newly developed strategy leads to a flexible controller with a simple structure that is easy to implement and consequently it can be constructive for the real world power system. The proposed method is tested on a three-area hydro-thermal power system in consideration with Generation Rate Constraint (GRC) for different contracted scenarios under diverse operating conditions. The results of the proposed controller are evaluated with the Hybrid Particle Swarm Optimisation (HCPSO), Real Coded Genetic Algorithm (RCGA) and Artificial Neural Network (ANN) controllers to illustrate its robust performance.

Optimal allocation of Distributed Generations (DGs) is one of the major problems of distribution utilities. Optimum locations and sizes of DG sources have profoundly created impact on system losses, voltage profile, and voltage stability of a distribution network. In this paper Quasi-Oppositional Swine Influenza Model Based Optimization with Quarantine (QOSIMBO-Q) has been applied to solve a multi-objective function for optimal allocation and sizing of DGs in distribution systems. The objective is to minimize network power losses, achieve better voltage regulation and improve the voltage stability within the frame-work of the system operation and security constraints in radial distribution systems. The limitation of SIMBO-Q algorithm is that it takes large number of iterations to obtain optimum solution in large scale real systems. To overcome this limitation and to improve computational efficiency, quasi-opposition based learning (QOBL) concept is introduced in basic SIMBO-Q algorithm. The proposed QOSIMBO-Q algorithm has been applied to 33-bus and 69-bus radial distribution systems and results are compared with other evolutionary techniques like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), combined GA/PSO, Teaching Learning Based Optimization (TLBO) and Quasi-Oppositional Teaching Learning Based Optimization (QOTLBO). Numerical studies represent the effectiveness and out-performance of the proposed QOSIMBO-Q algorithm.

Energy management system operators perform regular outage simulations in order to ensure secure operation of power systems. AC power flow based outage simulations are not preferred because of insufficient computational speed. Hence several outage models and computational methods providing acceptable accuracy have been developed. On the other hand double branch outages are critical rare events which can result in cascading outages and system collapse. This paper presents a double branch outage model and formulation of the phenomena as a constrained optimization problem. Optimization problem is then solved by using differential evolution method and particle swarm optimization algorithm. The proposed algorithm is applied to IEEE test systems. Computational accuracies of differential evolution based solutions and particle swarm optimization based solutions are discussed for IEEE 30 Bus Test System and IEEE 118 Bus Test System applications. IEEE 14 Bus Test System IEEE 30 Bus Test System IEEE 57 Bus Test System IEEE 118 Bus Test System and IEEE 300 Bus Test System simulation results are compared to AC load flows in terms of computational speed. Finally the performance of the proposed method is analyzed for different outage configurations. (C) 2015 Elsevier Ltd. All rights reserved.

This paper describes the fully-informed particle swarm optimization based economic dispatch among hydro-thermal units and compares the results with those obtained from existing heuristic and non-heuristic techniques. The short-term hydro-thermal scheduling is optimized using the meta-heuristic fully-informed particle swarm optimization (FIPSO) which is a variant of the canonical particle swarm optimization (CPSO). The FIPSO helps in finding a good approximation of an optimal solution for nonlinear multi-modal optimization problems by searching the complete search space. A global best (g-best) neighbourhood topology is compared with a local best (l-best) neighbourhood topology to describe the impact of particles’ neighbourhood on the convergence behaviour of the FIPSO algorithm. A standard two-generating-unit based system has been used to demonstrate the effectiveness of the FIPSO in economic scheduling of hydro and thermal units. The results, when compared with those from the literature, reveal the superiority of the proposed FIPSO algorithm.

This paper represents design of output feedback sliding mode controller (SMC) for multi area multi-source interconnected power system. After designing output feedback SMC, teaching and learning based optimization (TLBO) technique is utilized to optimize feedback gain and switching vector of the controller. The superiority of the proposed approach is shown by comparing the result with output feedback tuned SMC with differential evolution and particle swarm optimization and state feedback SMC tuned with genetic algorithm for a two area thermal interconnected power system. Further, the proposed approach is extended to multi-area multi-source non linear automatic generation control (AGC) system with/without HVDC link. First area consists up thermal, hydro and gas; second area consists up thermal, hydro and nuclear as generating unit. Additionally, the superiority of proposed approach is shown by sensitivity analysis, which is carried out with wide changes in system parameters.

A new optimization technique called Cuckoo Search (CS) algorithm for optimum tuning of PI controllers for Load Frequency Control (LFC) is suggested in this paper. A time domain based-objective function is established to robustly tune the parameters of PI-based LFC which is solved by the CS algorithm to attain the most optimistic results. A three-area interconnected system is investigated as a test system under various loading conditions where system nonlinearities are taken into account to confirm the effectiveness of the suggested algorithm. Simulation results are introduced to show the enhanced performance of the developed CS based controllers in comparison with Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and conventional integral controller. These results denote that the proposed controllers offer better performance over others in terms of settling times and various indices.

This paper improves the voltage sag for a distributed generation (DG) system with three-phase to two-phase connected transformers. The proposed connection system is compared with the systems designed by Scott and Le Blanc. A particle swarm optimization method with nonlinear time-varying evolution (PSO-NTVE) is used to determine the design values. This analysis considers sensitive loads and the coordination of over-current relays. The model uses a node admittance matrix for voltage sag calculation, in order to calculate the severity of system sag after single or two-phase faults. The analytical equations are useful in the mathematical optimal planning method. The PSO-NTVE is then used to derive suitable transformer impedances and relay time multiplier parameter values. The search for a global optimal solution for the voltage sag problems involves tests on the Scott and Le Blanc connected transformer power systems, in order to verify the effectiveness of the proposed method. Finally, the results show that the severity of the voltage sag is reduced by tuning the transformer impedances and relay settings.

The economic emission dispatch (EED) problem of thermal generating units is a highly complex combinatorial multi-constraint, non-convex optimization problem with conflicting objectives. This paper presents a Modulated Particle Swarm Optimization (MPSO) method to solve the EED problem of thermal units. The conventional PSO is modified by modulating velocity of particles for better exploration and exploitation of the search space. The modulation of particles’ velocity is controlled by introducing a truncated sinusoidal constriction function in the control equation of PSO. The conflicting objectives of the EED problem are combined in fuzzy framework by suggesting adjusted fuzzy membership functions which is then optimized using proposed PSO. The effectiveness of the proposed PSO is tested on three standard test generating systems considering several operational constraints like valve point effect, and prohibited operating zones (POZs). The application results and their comparison with other existing methods show that the proposed MPSO is promising for EED problem of thermal generating units.

This paper presents a novel teaching learning based optimization (TLBO) technique to solve economic load dispatch (ELD) of the thermal unit without considering transmission losses. The proposed methodology can take care of ELD considering nonlinearity such as valve point loading. The objective of economic load dispatch is to determine the optimal power generation of the units to meet the load demand, such that the overall cost of generation is minimized, while satisfying different operational constraints. TLBO is a recently developed evolutionary algorithm based on two basic concepts of education namely teaching phase and learning phase. At first, learners improve their knowledge through the teaching methodology of teacher and finally learners increase their knowledge by interactions among themselves. The effectiveness of the proposed algorithm has been verified on three different test systems with equality and inequality constraints. Compared with the other existing techniques demonstrates the superiority of the proposed algorithm.

The active and reactive power flow in distribution networks can be effectively controlled by optimally placing Shunt Capacitors (SCs) and Distributed Generators (DGs). This paper presents improved versions of three evolutionary or swarm-based search algorithms, namely, Improved Genetic Algorithm (IGA), Improved Particle Swarm Optimization (IPSO) and Improved Cat Swarm Optimization (ICSO) to efficiently handle the problem of simultaneous allocation of SCs and DGs in radial distribution networks while considering variable load scenario. The benefit of network reconfiguration has also been taken into account after optimal allocation of these devices. Several algorithm specific modifications are suggested in the standard forms of GA, PSO and CSO to overcome their inherent drawbacks. In addition, an intelligent search approach is proposed to enhance overall performance of proposed algorithms. The proposed methods are investigated on IEEE 33-bus and 69-bus test distribution systems showing promising results when compared with other recently established methods. Application results also show a marked improvement in the performance of these algorithms while compared with their respective standard counterparts.

Power systems are faced with a huge number of uncertainties, which should be addressed in the operational analyses. In this paper, an improved transformation method is used to consider the uncertainties of random variables in probabilistic load flow (PLF) problems. This method transforms non-normal input random variables with non-linear dependence to appropriate independent reference variables. The proposed transformation overcomes the point estimation method (PEM) limitations in estimating high statistical moments of PLF outputs. The proposed PLF method is examined on IEEE 14-bus and 118-bus test systems, consisting of uncertain loads, wind farms, and PV power plant generation. The results of Monte Carlo Simulation (MCS) are used as a benchmark. The results are compared with Nataf transformation, Rotational transformation, and particle swarm optimization clustering methods to prove the accuracy and efficiency of the proposed method.

This paper proposes a collaborative optimization allocation strategy for voltage-detection active power filters (VDAPFs) with multiple functions and static var generators (SVGs) to improve voltage harmonic (VH) and voltage deviation (VD) of distribution networks (DNs). The integrated voltage sensitivity is defined considering VH and VD simultaneously. According to the local characteristic of power quality (PQ), a partition algorithm based on integrated voltage sensitivity is developed to divide a network into several regions. Two methods based on single and integrated sensitivity are presented to determine dominant nodes as candidates of VDAPFs and SVGs in each region. The corresponding allocations of VDAPFs and SVGs are respectively provided considering the two methods. The sites and sizes of VDAPFs and SVGs are optimized through minimizing the total cost including investment and operation costs by improved particle swarm optimization (PSO) algorithm. An integrated evaluation index based on economy and PQ disturbance mitigation effect is defined to evaluate the performance of the two allocations, thereby determining an optimal allocation. The feasibility and effectiveness of the proposed strategy are verified implemented on IEEE 33-bus system and IEEE 69-bus system by comparing the two allocations with each other and with the conventional allocation strategy.

This paper presents the state space modelling of doubly fed induction generator (DFIG) for small signal stability assessment. The gains of PI controller in torque and voltage control loop of rotor-side converter (RSC) are optimized by particle swarm optimization (PSO) to improve the dynamic performance of DFIG. These optimized parameters results in improved damping of DFIG and minimizes the oscillations in rotor currents and electromagnetic torque. The nature of modes of oscillations for DFIG integrated to infinite bus are analysed under different operating conditions such as varying wind speed and grid strength. The transient analysis with optimized parameters shows the enhancement in LVRT capability during voltage sag and three phase fault as desired by grid codes.

In this paper, two new algorithms are implemented to solve optimal placement of capacitors in radial distribution systems in two ways that is, optimal placement of fixed size of capacitor banks (Variable Locations Fixed Capacitor banks-VLFQ) and optimal sizing and placement of capacitors (Variable Locations Variable sizing of Capacitors-VLVQ) for real power loss minimization and network savings maximization. The two bio-inspired algorithms Bat Algorithm (BA) and Cuckoo Search (CS): search for all possible locations in the system along with the different sizes of capacitors, in which the optimal sizes of capacitor are chosen to be standard sizes that are available in the market. To check the feasibility, the proposed algorithms are applied on standard 34 and 85 bus radial distribution systems. And the results are compared with results of other methods like Particle Swarm Optimization (PSO), Harmonic Search (HS), Genetic Algorithm (GA), Artificial Bee Colony (ABC), Teaching Learning Based Optimization (TLBO) and Plant Growth Simulation Algorithm (PGSA), as available in the literature. The proposed approaches are capable of producing high-quality solutions with good performance of convergence. The entire simulation has been developed in MATLAB R2010a software.

In electric power systems, finding optimal location and setting of flexible AC transmission system (FACTS) devices represents a difficult optimisation problem. This is due to its discrete, multi-objective, multi-modal and constrained nature. Finding near-global solutions in such a problem is very demanding. Brainstorm optimisation algorithm (BSOA) is a novel promising heuristic optimisation algorithm inspired by brainstorming process in human beings. In this paper, BSOA is employed to find optimal location and setting of FACTS devices. Static var compensators (SVC’s) and thyristor controlled series compensators (TCSC’s) are used as FACTS devices. FACTS allocation problem is formulated as a multi-objective problem whose objectives are voltage profile enhancement, overload minimisation and loss minimisation. The results of applying BSOA to FACTS allocation problem in IEEE 57 bus system demonstrate its high efficacy in solving this problem both with TCSC and SVC units. BSOA leads to better voltage profile and lower losses than particle swarm optimisation (PSO), genetic algorithm (GA), differential evolution (DE), simulated annealing (SA), hybrid of genetic algorithm and pattern search (GA–PS), backtracking search algorithm (BSA), gravitational search algorithm (GSA) and asexual reproduction optimisation (ARO). The findings of this research can be used by power system decision makers in order to establish a better voltage profile and lower voltage deviations during contingencies.

This paper proposes a reconfiguration methodology based on a cuckoo search algorithm (CSA) for minimizing active power loss and the maximizing voltage magnitude. The CSA method is a new meta-heuristic algorithm inspired from the obligate brood parasitism of some cuckoo species which lay their eggs in the nests of other host birds of other species for solving optimization problems. Compared to other methods, CSA method has fewer control parameters and is more effective in optimization problems. The effectiveness of the proposed CSA has been tested on three different distribution network systems and the obtained test results have been compared to those from other methods in the literature. The simulation results show that the proposed CSA can be an efficient and promising method for distribution network reconfiguration problems.

This paper proposes a new method of optimization, the Big Bang–Big Crunch (BB–BC) method, for the optimization of preventive and corrective control actions to enhance the dynamic security of a power system against transient instabilities. The control actions, generation rescheduling and load shedding, are considered as constrained optimization problems with static and dynamic security constraints. These optimization problems are solved through the BB–BC method to minimize some operational costs related to the control actions. To reduce the size of the search spaces and the computational burden, decision trees and correlation coefficients are used as feature selection tools, which determine the most effective generators and loads for shaping the system’s transient stability. The proposed method is applied to a test system and compared with genetic algorithms, particle swarm optimization, differential evolution and active set method. The BB–BC method is promising since it gives comparable results with the other population based optimization methods.

The purpose of this paper is to design a good tracking controller for the generator Automatic Voltage Regulator (AVR) system. A fuzzy logic-based controller that is called Fuzzy P + Fuzzy I + Fuzzy D (FP + FI + FD) controller has been designed optimally and applied to AVR system. In the proposed method, optimal tuning of controller parameters is very important to achieve the desired level of robust performance. Thus, a hybrid of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) (HGAPSO) technique has been used to find a better fuzzy system control. The motivation for using this hybrid method is to increase disturbance rejection effort, reduce fuzzy system efforts and take large parametric uncertainties into account. The developed FP + FI + FD control strategy leads to a flexible controller with simple structure that is easy to implement. The simulation results have been compared with the conventional Proportional–Integral–Derivative (PID) and fuzzy PID controllers. Three cases of simulation have been performed, case 1: comparing the tracking capability of the controllers, case 2: comparing the disturbance rejection capability of the controller and case 3: evaluating the performance of the controllers assuming that amplifier and exciter system parameters have 50% uncertainty. The simulation results shows that the proposed parallel FP + FI + FD controller has good performance from the perspective of overshoot/undershoot, settling time, and rise time in comparison with both conventional and fuzzy PID controllers.

The main objective of the short-term hydrothermal generation scheduling (SHGS) problem is to determine the optimal strategy for hydro and thermal generation in order to minimize the fuel cost of thermal plants while satisfying various operational and physical constraints. Usually, SHGS is assumed for a 1 day or a 1 week planing time horizon. It is viewed as a complex non-linear, non-convex and non-smooth optimization problem considering valve point loading (VPL) effect related to the thermal power plants, transmission loss and other constraints. In this paper, a modified dynamic neighborhood learning based particle swarm optimization (MDNLPSO) is proposed to solve the SHGS problem. In the proposed approach, the particles in swarm are grouped in a number of neighborhoods and every particle learns from any particle which exists in current neighborhood. The neighborhood memberships are changed with a refreshing operation which occurs at refreshing periods. It causes the information exchange to be made with all particles in the swarm. It is found that mentioned improvement increases both of the exploration and exploitation abilities in comparison with the conventional PSO. The presented approach is applied to three different multi-reservoir cascaded hydrothermal test systems. The results are compared with other recently proposed methods. Simulation results clearly show that the MDNLPSO method is capable of obtaining a better solution.

Differential evolution algorithm (DEA) is an efficient and powerful population-based stochastic search technique for solving optimization problems over continuous space, which has been proved to be a promising evolutionary algorithm for solving the ORPD problem and many engineering problems. However, the success of DEA in solving a specific problem crucially depends on appropriately choosing trial vector generation strategies (mutation strategies) and their associated control parameter values. This paper presents a differential evolution technique with various trial vector generation strategies based on optimal reactive power dispatch for real power loss minimization in power system. The proposed methodology determines control variable settings such as generator terminal voltages, tap positions and the number of shunts compensator to be switched, for real power loss minimization in the transmission systems. The DE method has been examined and tested on the IEEE 14-bus, 30-bus and the equivalent Algerian electric 114-bus power system. The obtained results are compared with two other methods, namely, interior point method (IPM), Particle Swarm Optimization (PSO) and other methods in the literature. The comparison study demonstrates the potential of the proposed approach and shows its effectiveness and robustness to solve the ORPD problem.

This paper presents a Hybrid Particle Swarm Optimization based Genetic Algorithm and Hybrid Particle Swarm Optimization based Shuffled Frog Leaping Algorithm for solving long-term generation maintenance scheduling problem. In power system, maintenance scheduling is being done upon the technical requirements of power plants and preserving the grid reliability. The objective function is to sell electricity as much as possible according to the market clearing price forecast. While in power system, technical viewpoints and system reliability are taken into consideration in maintenance scheduling with respect to the economical viewpoint. It will consider security constrained model for preventive Maintenance scheduling such as generation capacity, duration of maintenance, maintenance continuity, spinning reserve and reliability index are being taken into account. The proposed hybrid methods are applied to an IEEE test system consist of 24 buses with 32 thermal generating units.

Stabilizing area frequency and tie-line power oscillations in interconnected power systems are main concerns that have received significant attention in automatic generation control (AGC) studies. This paper deals with modeling and simulation of thyristor controlled series capacitor (TCSC) based damping controller in coordination with AGC to damp the oscillations and thereby, improve the dynamic stability. The contribution of TCSC in tie-line power exchange is formulated analytically for small perturbation and a systematic method based on the Taylor series expansion is proposed for modeling of TCSC based damping controller. The integral gains of AGC and TCSC parameters are optimized simultaneously using an improved particle swarm optimization (IPSO) algorithm through minimizing integral of time multiplied squared error (ITSE) performance index. The performance of the proposed TCSC–AGC coordinated controller is compared with case of AGC alone. A two-area interconnected multi-source power system, including TCSC located in series with the tie-line, is studied considering nonlinearity effects of generation rate constraint (GRC) and governor dead band (GDB). Simulation results show that proposed controller shows greater performance in damping of the oscillations and enhancing the frequency stability. Furthermore, sensitivity analyses are carried out against system loading condition, parametric uncertainties, and different perturbation patterns to show the robustness of TCSC–AGC.

This paper focuses on exploiting two computational intelligence techniques such as artificial neural network and evolutionary computation techniques in estimation of harmonics in power system. Accurate estimation of harmonics in distorted power system current/voltage signal is essential to effectively design filters for eliminating harmonics. No standard design is available for handling of local minima and training of NN but Evolutionary Computation (EC) techniques are capable of resolving local minima. Neural Network and Evolutionary Computing (Bacterial Foraging Optimization (BFO)) are combined to achieve accurate estimation of different harmonics components of a distorted power system signal. First estimation of unknown parameters are carried out using BFO, then optimized output of BFO are taken as initial values of the unknown parameters for Adaline. Amplitude and phase of fundamental and harmonics components are determined from final updated values of unknown parameters using Adaline. This Adaline based Bacterial Foraging Optimization (Adaline-BFO) hybrid estimation algorithm addresses the problems of slow convergence and reduction of time generation of off-springs happening in Genetic Algorithm (GA), and to avoid local minima in Particle Swarm Optimization (PSO). The proposed Adaline-BFO algorithm has been applied for estimation of harmonics of the voltage obtained across the inverter terminals of a prototype Photovoltaic (PV) system. From the obtained results, it is confirmed that the proposed Adaline-BFO algorithm provides superior estimation performance in terms of improvement in % error in estimation, processing time in computation and performance in presence of inter and sub-harmonic components when compared with the Discrete Fourier Transform (DFT), Kalman Filter (KF) and BFO algorithms.

Capacitor placement plays an important role in distribution system planning and operation. In distribution systems of electrical energy, banks of capacitors are widely installed to compensate the reactive power, reduce the energy loss in system, voltage profile improvement, and feeder capacity release. The capacitor placement problem is a combinatorial optimization problem having an objective function composed of power losses and capacitor installation costs subject to bus voltage constraints. Recently, many approaches have been proposed to solve the capacitor placement problem as a mixed integer programming problem. This paper presents a new capacitor placement method which employs particle swarm optimization (PSO) approaches with operators based on Gaussian and Cauchy probability distribution functions and also in chaotic sequences for a given load pattern of distribution systems. The proposed approaches are demonstrated by two examples of application. Simulation results show that the proposed method can achieve an optimal solution as the exhaustive search can but with much less computational time.

The paper uses particle swarm optimization (PSO) to determine the optimum switching angles of cascaded H-bridge multilevel inverter (CH-MLI) so as to produce the required fundamental voltage and reduce the harmonic content. This is done by solving the transcendental equations characterizing the harmonic content. The validity of the proposed method is verified through simulation studies for three-phase, five-level CH-MLI. To compare the results obtained using PSO, the simulation studies have been extended for three-phase, five-level CH-MLI using sinusoidal pulse width modulation (SPWM). The results obtained using PSO are found superior as compared to SPWM in terms of total harmonic distortion at different modulation indices.

In this paper, a novel hybrid Particle Swarm Optimization (PSO) and Pattern Search (PS) optimized fuzzy PI controller is proposed for Automatic Generation Control (AGC) of multi area power systems. Initially a two area non-reheat thermal system is used and the gains of the fuzzy PI controller are optimized employing a hybrid PSO and PS (hPSO-PS) optimization technique. The superiority of the proposed fuzzy PI controller has been shown by comparing the results with Bacteria Foraging Optimization Algorithm (BFOA), Genetic Algorithm (GA), conventional Ziegler Nichols (ZN), Differential Evolution (DE) and hybrid BFOA and PSO based PI controllers for the same interconnected power system. Additionally, the proposed approach is further extended to multi source multi area hydro thermal power system with/without HVDC link. The superiority of the proposed approach is shown by comparing the results with some recently published approaches such as ZN tuned PI, Variable Structure System (VSS) based ZN tuned PI, GA tuned PI, VSS based GA tuned PI, Fuzzy Gain Scheduling (FGS) and VSS based FGS for the identical power systems. Further, sensitivity analysis is carried out which demonstrates the ability of the proposed approach to wide changes in system parameters, size and position of step load perturbation The proposed approach is also extended to a non-linear power system model by considering the effect of governor dead band non-linearity and the superiority of the proposed approach is shown by comparing the results of hybrid BFO-PSO and craziness based PSO approach for the identical interconnected power system. Finally, the study is extended to a three area system considering both thermal and hydro units with different controllers in each area and the results are compared with hybrid BFO-PSO and ANFIS approaches.

Many electrical events can be easily damage electrical equipments in power systems. Such events or faults can be easily stopped at incipient steps but because of weakness of protecting systems they grow and extend, and consequently impose so many problems and cost to utilities. Power transformers are one of the vital equipments in electrical networks and industries, although many protecting systems have been implemented to prevent dangerous electrical faults, but most of them suffer many problems, such as; time wasting, computational burden, and low speed in response. In addition, whenever patterns of fault signals are similar, their discrimination from each other is so hard. Magnetizing inrush current, internal fault, CT saturation and over excitation are common electrical faults in power transformers so that most of the time are difficult to be separated. In this paper all considerations for designing perfect protecting system for power transformers are considered. Using intelligent approach, Artificial Neural Network (ANN) based method is designed. Indeed, proposed protecting system includes two major sections. In the first section, using Bayesian Classifier (BC) which works based on Bayesian rules and uses knowledge of training data directly; internal fault is detected and is discriminated from three other mentioned faults and normal condition. If the event is not internal fault, second condition of this intelligent system makes a decision. In this section, ANN trained by swarm based algorithms, namely; Improved Gravitational Search Algorithm (IGSA) or Particle Swarm Optimization (PSO) is used to discriminate magnetizing inrush current, current transformer (CT) saturation and over excitation. Obtained results show that proposed system can easily and precisely follow the electrical faults in power transformer and detect them at incipient steps. Such quick and accurate response helps to save so much energy, financial cost and time.

This paper presents an improved maximum power point tracking (MPPT) strategy for photovoltaic (PV) systems based on particle swarm optimization (PSO). The capability of the PSO algorithm to cope with partially shaded conditions (PSCs) is the primary motivation of this research. Unlike conventional PSO-based MPPT systems, a variable sampling time strategy (VSTS) based on the investigation of the dynamic behavior of converter current is deployed to increase system tracking time. The performance of the proposed system is evaluated using MATLAB simulation and experimentation, in which a digital signal controller is used to implement the proposed algorithm on a real boost converter connected to a PV simulator. The main advantage of the proposed algorithm is fast and accurate performance under different conditions, including PSCs.

Allocation of flexible AC transmission systems (FACTS) devices is a challenging power system problem. This paper proposes a new particle swarm optimisation (PSO) variant, called enhanced leader PSO (ELPSO), for solving this problem. This algorithm is capable of solving FACTS allocation problem in a way leading to lower amounts of power flow violations, voltage deviations and power losses with respect to other optimisation algorithms. Distributed thyristor controlled series compensators (D-TCSC’s) are used. D-TCSC’s are installed at all branches except those with regulating transformers. The reactances of D-TCSC’s are found in optimisation process. ELPSO features a five-staged successive mutation strategy which mitigates premature convergence problem of conventional PSO. ELPSO and other optimisation algorithms are applied to IEEE 14 bus and 118 bus power systems for N-1 contingencies and also for simultaneous outage of four branches. The results show that it leads to lower amounts of power flow violations, voltage deviations and power losses with respect to conventional PSO (CPSO) and eight other optimisation algorithms including genetic algorithm (GA), gravitational search algorithm (GSA), galaxy based search algorithm (GBSA), invasive weed optimisation (IWO), asexual reproduction optimisation (ARO), threshold acceptance (TA), pattern search and nonlinear programming (NLP).

The economical use of fuel available for the generation of power has become a major concern of electric utilities. This paper presents an approach for economic fuel scheduling problem by using group search optimization. This is a minimization technique that includes the standard load constraints as well as the fuel constraints. The generation schedule is compared to that which would result if fuel constraints were ignored. The comparison shows that fuel consumed can be adequately controlled by adjusting the power output of various generating units so that the power system operates within its fuel limitations and within contractual constraints. It has been found that small additional amount of fuel may be required to serve the same power demand but the additional cost of this fuel may well compensate for the penalty that might otherwise be imposed for not maintaining the fuel contract. Numerical results for two test systems have been presented and the test results obtained from group search optimization are compared with those obtained from particle swarm optimization and evolutionary programming.

This paper introduces a novel hybrid AC/DC microgrid design based on distribution FACTS devices. The objective of this design is to enhance the microgrid operation performance such as stabilize the buses voltage, reduce the feeder losses, improve the power factor and mitigate the harmonic distortion using the distribution FACTS devices. In the proposed design of microgrid which includes PV, fuel cell, battery, micro gas turbine, wind turbine, and diesel engine within microgrid, Switched Filter Compensator (SFC) is presented as AC-side D-FACTS device and Green Plug Filter Compensator (GPFC) is introduced as DC-side D-FACTS devices. Optimization search method based particle swarm optimization (PSO) is developed to optimize the control gains of D-FACTS controllers to achieve an effective integration of distributed energy resources technologies within MGs and to achieve higher microgrid performance. The results shown are generated from MATLAB simulation of the presented microgrid, where comparisons of with and without FACTS modules are presented in grid connected and islanding operation modes. Results show superiority and improved performance using the presented distribution FACTS.

Dynamic economic dispatch of a microgrid is better suited to the requirements of a system in actual operation because it not only considers the lowest cost in a scheduling cycle but also coordinates between different distribution generations (DGs) over many periods. So it is very significant to research the dynamic economic dispatch of a microgrid. Since wind energy and solar energy are subject to random variations and intervals, there is great difficulty in solving the dynamic economic dispatch. In this paper, we establish a combined heat and power (CHP) microgrid system which includes wind turbines (WT), photovoltaic arrays (PV), diesel engines (DE), a micro-turbine (MT), a fuel cell (FC) and a battery (BS). Comprehensively considering the operation cost and the pollutant treatment cost of the microgrid system, we choose the maximum comprehensive benefits as the objective function for the dynamic economic dispatch. At the same time, we establish the spinning reserve probability constraints of the microgrid considering the influence of uncertainty factors such as the fluctuation of the renewable energy, load fluctuation error, and fault shutdown of the unit. Also researched are four different operation scheduling strategies under grid-connected mode and island mode of the microgrid. An improved particle swarm optimization (PSO) algorithm combined with Monte Carlo simulation is used to solve the objective function. With the example system, the proposed models and improved algorithm are verified. When the microgrid is running under the grid-connected mode, we discuss the influence of different scheduling strategies, optimization goals and reliability indexes on the dynamic economic dispatch. And when the microgrid is running under the island mode, we discuss the influence of the uncertainty factors and the capacity of the battery on the dynamic economic dispatch. The presented research can provide some reference for dynamic economic dispatch of microgrid on making full use of renewable energy and improving the microgrid system reliability.

The power distribution network design problem has a growing impact on secure and economical operation of distribution power system. This issue is well known as a non-linear, multi-modal and multi-objective optimization problem where global optimization techniques are required in order to avoid local minima. In this study, a new approach using seeker optimization algorithm (SOA) is proposed for distribution system planning problem with simultaneous placement of automatic reclosers (RAs), considering total system economic cost, overall system reliability, system power losses and voltage deviations as an objective functions. Normally, conventional power distribution systems (CPDS) are radial in nature and momentary fault in the system causes large area of the grid to be blacked out, which leads to huge load interruptions. Reliable distribution system minimize this effect by allowing faults to clear themselves by protection device operations such as automatic reclosers (RAs) and quickly restores the power through system reconfiguration by minimizing the loads affected. Thus, in order to prevent the system from momentary faults, simultaneous placement of RAs has been also done which leads to a design of an advanced power distribution system (APDS). Thus, the reliability issues are of major importance for effective planning of an APDS. For evaluation of reliability measure, the contingency-load-loss index (CLLI) is assessed in this paper, which is independent of the failure rate and fault repair duration of the feeder branches. The performance of the proposed algorithm is extensively assessed and comparisons are made with Particle Swarm Optimization (PSOs), Genetic Algorithm (GA) and graph theoretical approach (GTA) applied on the 54 and 100-bus primary power distribution systems. The simulation results show that the proposed approach performed better than the other listed algorithms and can be efficiently used for the optimal design of an advanced power distribution system.

The goal of this paper is to present an algorithm for increasing output power in wind farms with high wake losses. In some wind directions, wake effects cannot be neglected because the distance between turbines may be less than 5–8 times of rotor diameter. The purpose of presented algorithm is to determine coefficient of performance (CP), thrust coefficient (CT), pitch angle (β) and rotational speed (ω) of each turbine by using wake effects equations so that wake losses in the wind farm becomes minimum which result in increasing output power of wind farm. PSO algorithm is used for optimization. Finally, a sample wind farm consisting of 16 turbines is used as a case study and the results show that there is a noticeable increase in the amount of wind farm output power rather than the time which no control and optimization is used. The conclusion is that the presented algorithm is suitable for wind farms with high wake losses and little distance between their turbines and is very effective for increasing their output power.

This paper presents a load shedding algorithm for alleviating line overloads employing Teaching learning based optimization (TLBO). The buses are selected for load shed based on the sensitivity of severity index with respect to load shed. Load shed is based on the next interval predicted load which could cause emergency situation from thermal limit consideration. Line flow constraints have not only considered for next predicted interval but in present base case loading conditions also. Optimum load shed at the selected buses have been obtained for 30-bus, 39-bus standard test systems. Further another technique modified version of bare bones particle swarm optimization known as (BBExp) has been used to validate the algorithm for load shedding.

This paper presents the implementation of the Firefly Algorithm (FA) with an online wavelet filter on the automatic generation control (AGC) model for a three unequal area interconnected reheat thermal power system. The model includes time delay, dead zone, boiler, Generation Rate Constraint (GRC), and high frequency noise components. A novel filtering technique based on wavelet transform is introduced for the purpose of removing noise(s) from the ACE signal. The performance of the filter is measured by formulating a signal integrity index. The simulation results show that the FA is able to outperform the Particle Swarm Optimization (PSO) in obtaining the minimum objective function based on Integral of Time Weighted Squared Error (ITSE). The results also shows that the proposed online wavelet filter performs with a higher degree of efficiency compared to the conventional low pass filter when the practical model of the AGC is analyzed. Further investigation by varying the GRC and time delay parameter confirms the robustness of the FA tuned controller with the online wavelet filter.

In this paper, a hybrid approach for tuning and placement of power system stabilizers (PSS) in multi-machine power systems is provided with the aim of reducing low-frequency oscillations (LFO) and improving power system dynamic stability with wide range of changes in system parameters and operating point of the system. PSS parameters are adjusted using Particle Swarm Optimization algorithm (PSO), and using Takagi–Sugeno (TS) fuzzy, optimal location for the PSS is determined. The employed fuzzy system has two inputs, the real part and the damping coefficient of network eigenvalues. The results of test on a grid four machine-two area sample, show optimal performance of the proposed method in improving system stability and reducing low-frequency oscillations of local and inter-area modes.

Loss reduction is one of the prime objectives for planning of distributed generation (DG). To improve performance of distribution systems, optimal placement of distributed generator is critically important, as DG benefits are site and size specific. Optimal placement of multiple DG units is non-convex and non-linear optimization problem. The methodology proposed in this paper solves Mixed Integer Non-Linear Programming (MINLP) formulation for loss minimization. The proposed methodology simplifies the problem by dividing it in two phases, namely Siting Planning Model (SPM) and Capacity Planning Model (CPM) thereby reducing the search space and computational time. The SPM model selects the candidate buses based on Combined Loss Sensitivity (CLS). In CPM, optimal locations and sizes are obtained by integrating Sequential Quadratic Programming (SQP) and Branch and Bound (BAB) algorithm for MINLP problem. The performance of the proposed method is tested on IEEE 33-bus and IEEE 69-bus distribution system for placement of single and multiple DG units capable of delivering either real or both real and reactive power. The obtained results are then compared with three basic classes of optimization methods, viz., Exhaustive Load Flow (ELF), Improved Analytical (IA) and Particle Swarm Optimization (PSO). The key advantage of the proposed method is that it simultaneously considers placement of multiple DG units giving better optimal solution in reasonably less computational time.

This paper describes the power quality improvement using Particle Swarm Optimization for optimal placement and sizing of Distribution Static Synchronous Series Compensator in radial distribution networks. In this analysis, enhancement of power quality includes improvement of voltage and reduction of line losses. A complete performance analysis is carried out on 12 and 69 bus radial distribution systems to demonstrate the effectiveness of the proposed work. The results show that the placement of DSSSC in radial distribution systems effectively improves the voltage of a system and reduces the power losses. MATLAB, Version 7.10 software is used for simulation.

A Particle Swarm Optimization algorithm for finding the optimal location and sizing of Distributed Generation and Distribution STATicCOMpensator (DSTATCOM) with the aim of reducing the total power loss along with voltage profile improvement of Radial Distribution System is proposed in this paper. The new-fangled formulation projected is inspired by the idea that the optimum placement of the DG and DSTATCOM can facilitate in minimization of the line loss and voltage dips in Radial Distribution Systems. A complete performance analysis is carried out on 12, 34 and 69 bus radial distribution test systems and each test system has five different cases. The results analyzed using Loss Sensitivity Factor shows the optimal placement and sizing of DG and DSTATCOM in Radial Distribution System effectively improves the voltage profile and reduces the total power losses of the system.

Context: Current decision development in electricity market needs a variety of forecasting techniques to analysis the nature of electric load series. And the interpretability and forecasting accuracy of the electric load series are two main objectives when establishing the load forecasting model. Objective: Considering that electric load series exhibit repeating seasonal cycles at different level ( daily, weekly and annual seasonality), this paper concerns the interpretability of these seasonal cycles and the forecasting accuracy. Method: For the above proposes, the author firstly introduces a multiple linear regression model that involves treating all the seasonal cycles as the input attributes. The result helps the managers to interpret the series structure with multiple seasonal cycles. To improve the forecasting accuracy, a support vector regression model based on optimal training subset (OTS) and adaptive particle swarm optimization (APSO) algorithm is established to forecast the residual series. Thus, a novel hybrid model combining the proposed linear regression model and support vector regression model is built to achieve the above bi-objective short-term load forecasting. Results: The effectiveness of the hybrid model is evaluated by an electrical load forecasting in California electricity market. The proposed modeling algorithm generates not only the seasonal cycle's decomposition for the time series, but also better accuracy predictions. Conclusion: It is concluded that the hybrid model provides a very powerful tool of easy implementation for bi-objective short-term electric load forecasting.

Unit commitment (UC) is a NP-hard nonlinear mixed-integer optimization problem. This paper proposes ELRPSO, an algorithm to solve the UC problem using Lagrangian relaxation (LR) and particle swarm optimization (PSO). ELRPSO employs a state-of-the-art powerful PSO variant called comprehensive learning PSO to find a feasible near-optimal UC schedule. Each particle represents Lagrangian multipliers. The PSO uses a low level LR procedure, a reserve repairing heuristic, a unit decommitment heuristic, and an economic dispatch heuristic to obtain a feasible UC schedule for each particle. The reserve repairing heuristic addresses the spinning reserve and minimum up/down time constraints simultaneously. Moreover, the reserve repairing and unit decommitment heuristics consider committing/decommitting a unit for a consecutive period of hours at a time in order to reduce the total startup cost. Each particle is initialized using the Lagrangian multipliers obtained from a LR that iteratively updates the multipliers through an adaptive subgradient heuristic, because the multipliers obtained from the LR tend to be close to the optimal multipliers and have a high potential to lead to a feasible near-optimal UC schedule. Numerical results on test thermal power systems of 10, 20, 40, 60, 80, and 100 units demonstrate that ELRPSO is able to find a low-cost UC schedule in a short time and is robust in performance.

An approach for optimal placement of flux-lock type SFCL with tap changer (TC-FLSFCL) and adjustment of tap changer related to it is used in this paper by debating the reduction of fault current and enhancement of reliability in a distribution network connected with distributed generation (DG). Recently, various superconducting fault current limiters (SFCL) have been developed in the field of power system. Especially, through researches, SFCLs have been put to practical use in distribution network as well as in the transmission network. TC-FLSFCL is a flexible SFCL that it has some preference than previous SFCLs. In this type of SFCL the current limiting characteristics are improved and the fault current limiting level during a fault period can be adjusted by controlling the current in the third winding, which also made the magnetic field applied to the high-Tc superconducting (HTSC) element. Three objective functions based on reliability index, reduction of fault current and the number of installed TC-FLSFCL are systematized and a nonlinear time-varying evolution (NTVE) based multi-objective particle swarm optimization (MOPSO) style is then formed in searching for the best location and tuning of tap changer of TC-FLSFCL to meet the fitness requirements. A decision-making procedure based on fuzzy decision making (FDM) is used for finding the best compromise solution from the set of Pareto-solutions obtained through MOPSO-NTVE. In a distribution network, Bus 4 of Roy Billinton test system (RBTS), comparative analysis of the results obtained from application of the resistive SFCL (RSFCL) and TC-FLSFCL is presented. The results show that optimal placement of TC-FLSFCL than RSFCL can improve reliability index and fault current reduction index with less number.

In this paper, the design of a conventional power system stabilizer (CPSS) is carried out using the bat algorithm (BA) to optimize its gain and pole-zero parameters. The proposed optimization of CPSS parameters is considered with an objective function based on eigenvalue shifting to guaranteeing the stability of nonlinear plant for a wide range of operating conditions of the single-machine infinite-bus power system model. The system performance with BA optimized CPSS (BA-CPSS) controller is compared with a particle swarm optimization based CPSS (PSO-CPSS) controller. The robustness is tested by considering several operating conditions (133 in numbers) to establish the superior performance with BACPSS over the PSO-CPSS.

This paper presents a novel solution for the optimal placement and sizing of active power conditioners in future smart distribution systems by using the dynamic discrete firefly algorithm. The proposed method aims to mitigate the power quality effects of hybrid renewable energy-based generators and electric vehicle stations in smart grids. A multi-objective optimization problem is formulated to improve the voltage profile, minimize the voltage total harmonic distortion, and reduce the total investment cost. The performance analysis of the proposed algorithm is conducted on a modified IEEE 16-bus test system by using Matlab software. The results are then compared with the conventional stationary firefly algorithm, hybrid improved genetic algorithm, and dynamic particle swarm optimization. The comparison proves that the proposed optimization algorithm is the most effective method among the other methods and that the proposed method can precisely determine the optimum location and size of active power conditioners in distribution systems.