Particle swarm optimization algorithm for simultaneous optimal placement and sizing of shunt active power conditioner(APC)and shunt capacitor in harmonic distorted distribution system

Due to development of distribution systems and increase in electricity demand,the use of capacitor banks increases.From the other point of view,nonlinear loads generate and inject considerable harmonic currents into power system.Under this condition if capacitor banks are not properly selected and placed in the power system,they could amplify and propagate these harmonics and deteriorate power quality to unacceptable levels.With attention of disadvantages of passive filters,such as occurring resonance,nowadays the usage of this type of harmonic compensator is restricted.On the other side,one of parallel multi-function compensating devices which are recently used in distribution system to mitigate voltage sag and harmonic distortion,performs power factor correction,and improves the overall power quality as active power conditioner(APC).Therefore,the utilization of APC in harmonic distorted system can affect and change the optimal location and size of shunt capacitor bank under harmonic distortion condition.This paper presents an optimization algorithm for improvement of power quality using simultaneous optimal placement and sizing of APC and shunt capacitor banks in radial distribution networks in the presence of voltage and current harmonics.The algorithm is based on particle swarm optimization(PSO).The objective function includes the cost of power losses,energy losses and those of the capacitor banks and APCs.

Power system stabilizer design using hybrid multi-objective particle swarm optimization with chaos

A novel technique for the optimal tuning of power system stabilizer (PSS) was proposed,by integrating the modified particle swarm optimization (MPSO) with the chaos (MPSOC).Firstly,a modification in the particle swarm optimization (PSO) was made by introducing passive congregation (PC).It helps each swarm member in receiving a multitude of information from other members and thus decreases the possibility of a failed attempt at detection or a meaningless search.Secondly,the MPSO and chaos were hybridized (MPSOC) to improve the global searching capability and prevent the premature convergence due to local minima.The robustness of the proposed PSS tuning technique was verified on a multi-machine power system under different operating conditions.The performance of the proposed MPSOC was compared to the MPSO,PSO and GA through eigenvalue analysis,nonlinear time-domain simulation and statistical tests.Eigenvalue analysis shows acceptable damping of the low-frequency modes and time domain simulations also show that the oscillations of synchronous machines can be rapidly damped for power systems with the proposed PSSs.The results show that the presented algorithm has a faster convergence rate with higher degree of accuracy than the GA,PSO and MPSO.

Bacterial graphical user interface oriented by particle swarm optimization strategy for optimization of multiple type DFACTS for power quality enhancement in distribution system

This study proposes a graphical user interface(GUI) based on an enhanced bacterial foraging optimization(EBFO) to find the optimal locations and sizing parameters of multi-type DFACTS in large-scale distribution systems.The proposed GUI based toolbox,allows the user to choose between single and multiple DFACTS allocations,followed by the type and number of them to be allocated.The EBFO is then applied to obtain optimal locations and ratings of the single and multiple DFACTS.This is found to be faster and provides more accurate results compared to the usual PSO and BFO.Results obtained with MATLAB/Simulink simulations are compared with PSO,BFO and enhanced BFO.It reveals that enhanced BFO shows quick convergence to reach the desired solution there by yielding superior solution quality.Simulation results concluded that the EBFO based multiple DFACTS allocation using DSSSC,APC and DSTATCOM is preferable to reduce power losses,improve load balancing and enhance voltage deviation index to 70%,38% and 132% respectively and also it can improve loading factor without additional power loss.

Power System Aggregate Load Area Modelling by Particle Swarm Optimization

This paper presents a new approach for deriving a power system aggregate load area model (ALAM). In this approach, an equivalent area load model is derived to represent the load characters for a particular area load of a power system network. The Particle Swarm Optimization (PSO) method is employed to identify the unknown parameters of the generalised system, ALAM, based on the system measurement directly using a one-step scheme. Simulation studies are carried out for an IEEE 14-Bus power system and an IEEE 57-Bus power system. Simulation results show that the ALAM can represent the area load characters accurately under different operational conditions and at different power system states.

Damping Controller Based Quantum Particle Swarm Optimization for VSC HVDC to Improve Power System Stability

The use of the supplementary controllers of a High Voltage Direct Current (HVDC) based on Voltage Source Converter (VSC) to damp low Frequency oscillations in a weakly connected system is surveyed. Also, singular value decomposition (SVD)-based approach is used to analyze and assess the controllability of the poorly damped electromechanical modes by VSC-HVDC different control channels. The problem of supplementary damping controller based VSC-HVDC system is formulated as an optimization problem according to the time domain-based objective function which is solved using quantum-behaved particle swarm optimization (QPSO). Individual designs of the HVDC controllers using QPSO method are evaluated. The effectiveness of the proposed controllers on damping low frequency oscillations is checked through eigenvalue analysis and non-linear time simulation under various disturbance conditions over a wide range of loading.

Scenario-oriented hybrid particle swarm optimization algorithm for robust economic dispatch of power system with wind power

An economic dispatch problem for power system with wind power is discussed.Using discrete scenario to describe uncertain wind powers,a threshold is given to identify bad scenario set.The bad-scenario-set robust economic dispatch model is established to minimize the total penalties on bad scenarios.A specialized hybrid particle swarm optimization(PSO)algorithm is developed through hybridizing simulated annealing(SA)operators.The SA operators are performed according to a scenario-oriented adaptive search rule in a neighborhood which is constructed based on the unit commitment constraints.Finally,an experiment is conducted.The computational results show that the developed algorithm outperforms the existing algorithms.

A Multi-Agent Particle Swarm Optimization for Power System Economic Load Dispatch

A new versatile optimization, the particle swarm optimization based on multi-agent system (MAPSO) is presented. The economic load dispatch (ELD) problem of power system can be solved by the algorithm. By competing and cooperating with the randomly selected neighbors, and adjusting its global searching ability and local exploring ability, this algorithm achieves the goal of high convergence precision and speed. To verify the effectiveness of the proposed algorithm, this algorithm is tested by three different ELD cases, including 3, 13 and 40 units IEEE cases, and the experiment results are compared with those tested by other intelligent algorithms in the same cases. The compared results show that feasible solutions can be reached effectively, local optima can be avoided and faster solution can be applied with the proposed algorithm, the algorithm for ELD problem is versatile and efficient.

Quantum particle swarm optimization for micro-grid system with consideration of consumer satisfaction and benefit of generation side

Considering comprehensive benefit of micro-grid system and consumers,we establish a mathematical model with the goal of the maximum consumer satisfaction and the maximum benefit of power generation side in the view of energy management.An improved multi-objective local mutation adaptive quantum particle swarm optimization(MO-LM-AQPSO)algorithm is adopted to obtain the Pareto frontier of consumer satisfaction and the benefit of power generation side.The optimal solution of the non-dominant solution is selected with introducing the power shortage and power loss to maximize the benefit of power generation side,and its reasonableness is verified by numerical simulation.Then,translational load and time-of-use electricity price incentive mechanism are considered and reasonable peak-valley price ratio is adopted to guide users to actively participate in demand response.The simulation results show that the reasonable incentive mechanism increases the benefit of power generation side and improves the consumer satisfaction.Also the mechanism maximizes the utilization of renewable energy and effectively reduces the operation cost of the battery.

Design of Robust Power System Stabilizer Based on Particle Swarm Optimization

In this paper, we examine the problem of designing power system stabilizer (PSS). A new technique is developed using particle swarm optimization (PSO) combined with linear matrix inequality (LMI). The main feature of PSO, not sticking into a local minimum, is used to eliminate the conservativeness of designing a static output feedback (SOF) stabilizer within an iterative solution of LMIs. The technique is further extended to guarantee robustness against uncertainties wherein power systems operation is changing continuously due to load changes. Numerical simulation ahs illustrated the utility of the developed technique.

Improved particle swarm optimization algorithm for multi-reservoir system operation

In this paper, a hybrid improved particle swarm optimization （IPSO） algorithm is proposed for the optimization of hydroelectric power scheduling in multi-reservoir systems. The conventional particle swarm optimization （PSO） algorithm is improved in two ways： （1） The linearly decreasing inertia weight coefficient （LDIWC） is replaced by a self-adaptive exponential inertia weight coefficient （SEIWC）, which could make the PSO algorithm more balanceable and more effective in both global and local searches. （2） The crossover and mutation idea inspired by the genetic algorithm （GA） is imported into the particle updating method to enhance the diversity of populations. The potential ability of IPSO in nonlinear numerical function optimization was first tested with three classical benchmark functions. Then, a long-term multi-reservoir system operation model based on IPSO was designed and a case study was carried out in the Minjiang Basin in China, where there is a power system consisting of 26 hydroelectric power plants. The scheduling results of the IPSO algorithm were found to outperform PSO and to be comparable with the results of the dynamic programming successive approximation （DPSA） algorithm.

Chaos quantum particle swarm optimization for reactive power optimization considering voltage stability

The reactive power optimization considering voltage stability is an effective method to improve voltage stablity margin and decrease network losses,but it is a complex combinatorial optimization problem involving nonlinear functions having multiple local minima and nonlinear and discontinuous constraints. To deal with the problem,quantum particle swarm optimization (QPSO) is firstly introduced in this paper,and according to QPSO,chaotic quantum particle swarm optimization (CQPSO) is presented,which makes use of the randomness,regularity and ergodicity of chaotic variables to improve the quantum particle swarm optimization algorithm. When the swarm is trapped in local minima,a smaller searching space chaos optimization is used to guide the swarm jumping out the local minima. So it can avoid the premature phenomenon and to trap in a local minima of QPSO. The feasibility and efficiency of the proposed algorithm are verified by the results of calculation and simulation for IEEE 14-buses and IEEE 30-buses systems.

Fault Diagnosis of Power Electronic Circuits Based on Adaptive Simulated Annealing Particle Swarm Optimization

In the field of energy conversion,the increasing attention on power electronic equipment is fault detection and diagnosis.A power electronic circuit is an essential part of a power electronic system.The state of its internal components affects the performance of the system.The stability and reliability of an energy system can be improved by studying the fault diagnosis of power electronic circuits.Therefore,an algorithm based on adaptive simulated annealing particle swarm optimization(ASAPSO)was used in the present study to optimize a backpropagation(BP)neural network employed for the online fault diagnosis of a power electronic circuit.We built a circuit simulation model in MATLAB to obtain its DC output voltage.Using Fourier analysis,we extracted fault features.These were normalized as training samples and input to an unoptimized BP neural network and BP neural networks optimized by particle swarm optimization(PSO)and the ASAPSO algorithm.The accuracy of fault diagnosis was compared for the three networks.The simulation results demonstrate that a BP neural network optimized with the ASAPSO algorithm has higher fault diagnosis accuracy,better reliability,and adaptability and can more effectively diagnose and locate faults in power electronic circuits.

Optimal Power Flow Solution Using Particle Swarm Optimization Technique with Global-Local Best Parameters

This paper proposes an efficient method for optimal power flow solution （OPF） using particle swarm optimization （PSO） technique. The objective of the proposed method is to find the steady state operation point in a power system which minimizes the fuel cost, while maintaining an acceptable system performance in terms of limits on generator power, line flow limits and voltage limits. In order to improvise the performance of the conventional PSO （cPSO）, the fine tuning parameters- the inertia weight and acceleration coefficients are formulated in terms of global-local best values of the objective function. These global-local best inertia weight （GLBestlW） and global-local best acceleration coefficient （GLBestAC） are incorporated into PSO in order to compute the optimal power flow solution. The proposed method has been tested on the standard IEEE 30 bus test system to prove its efficacy. The results are compared with those obtained through cPSO. It is observed that the proposed algorithm is computationally faster, in terms of the number of load flows executed and provides better results than the conventional heuristic techniques.

Structural Optimization and Analysis of Magnetic Coupled Inductive Power Transfer System

Along with the prosperous of magnetic coupled inductive power transfer( MCIPT) technology which is widely used in industrial applications such as electric vehicle charging,the topology of double D coils( DD coils) with a spatial quadrature Q coil arises with great research interest. The Q coil, however, has been thoroughly studied by adding to the receiving side but seldom to the transmitting side. By using finite element simulation, this paper presents a preliminary study on the effectiveness of Q coil in the transmitting side,and its inner dimension is optimized for optimal compensating the misalignment between the transmitting and receiver sides. Simulation results show that the windings of the Q coil should be placed in the center of the aperture of the DD coils,and these results render a useful guidance for mechanical structural design and circuit controller design of MCIPT.

Optimal Power System Restoration and Reconfiguration in Distribution Circuit Using BFAM and BPSO

This paper approaches the problem of restoring a faulted area in an electric power distribution system after locating and isolating the faulted block and reconfiguring the system. Through this paper we are going to explain the power system restoration technique using brute-force attack method (BFAM) and binary particle swarm optimization (BPSO). This is a technique based on the possible combination in mathematical analysis which is explained in the introduction. After isolating the fault, main concentration will be towards the reconfiguration of the restored system using BPSO. Here due to fault in the system near-by agent will be affected and become useless and will go in the non-working mode. Now in order to restore these near-by loads we will give a new connection called NO (Normally Open. Using these switch system will be restored with power availability. After restoration using the BFAM, the BPSO will be used in order to provide the stable configuration. The output of the BFAM will be used as input for the BPSO and then we will reconfigure our system in order to provide the stable configuration. The effectiveness of the proposed BFAM and BPSO is demonstrated by simulating tests in a proposed distribution network and verified the results using the Matlab and C programming.

Active Power Allocation of Virtual Synchronous Generator Using Particle Swarm Optimization Approach

In recent years, the penetration of renewable energy sources (RES) is increasing due to energy and environmental issues, causing several problems in the power system. These problems are usually more apparent in microgrids. One of the problems that could arise is frequency stability issue due to lack of inertia in microgrids. Lack of inertia in such system can lead to system instability when a large disturbance occurs in the system. To solve this issue, providing inertia support to the microgrids by a virtual synchronous generator (VSG) utilizing energy storage system is a promising method. In applying VSG, one important aspect is regarding the set value of the active power output from the VSG. The amount of allocated active power during normal operation should be determined carefully so that the frequency of microgrids could be restored to the allowable limits, as close as possible to the nominal value. In this paper, active power allocation of VSG using particle swarm optimization (PSO) is presented. The results show that by using VSG supported by active power allocation determined by the method, frequency stability and dynamic stability of the system could be improved.

Research on Reactive Power Optimization of Offshore Wind Farms Based on Improved Particle Swarm Optimization

The lack of reactive power in offshore wind farms will affect the voltage stability and power transmission quality of wind farms.To improve the voltage stability and reactive power economy of wind farms,the improved particle swarmoptimization is used to optimize the reactive power planning in wind farms.First,the power flow of offshore wind farms is modeled,analyzed and calculated.To improve the global search ability and local optimization ability of particle swarm optimization,the improved particle swarm optimization adopts the adaptive inertia weight and asynchronous learning factor.Taking the minimum active power loss of the offshore wind farms as the objective function,the installation location of the reactive power compensation device is compared according to the node voltage amplitude and the actual engineering needs.Finally,a reactive power optimizationmodel based on Static Var Compensator is established inMATLAB to consider the optimal compensation capacity,network loss,convergence speed and voltage amplitude enhancement effect of SVC.Comparing the compensation methods in several different locations,the compensation scheme with the best reactive power optimization effect is determined.Meanwhile,the optimization results of the standard particle swarm optimization and the improved particle swarm optimization are compared to verify the superiority of the proposed improved algorithm.

An Efficient Particle Swarm Optimization Technique for 4-Leg Shunt Active Power Filter

This paper describes the mitigation of harmonics in source and neutral current in three phase four wire system based on 4-leg shunt active power filter under balanced and unbalanced load conditions. Particle Swarm Optimization (PSO) and conventional Proportional Integral (PI) controller are used as control techniques to analyze the control performance of 4-leg shunt active power filter. The synchronous reference frame (SRF) method is used to extract reference current in 4-leg shunt active filter. The Hysteresis Current Controller (HCC) is used to generate gate pulses for Voltage Source Inverter (VSI) based 4-leg shunt active power filter. The proposed PSO technique gives less percentage of Total Harmonic Distortion (THD) value in source and neutral current and settling time of the DC capacitor voltage compared to conventional PI controller technique. The model of the proposed system performance was validated using MATLAB/Simulink environment.

Accelerated Particle Swarm Optimization for Controlling Virtual Power Plant Consisting of Renewable Energy Sources

RES （renewable energy sources）, such as wind and photovoltaic power plants, suffer from their stochastic nature that is why their behavior on market is very delicate. In order to diversify risk, a concept of VPP （virtual power plant） has been developed. The VPP is composed of several RES, from which at least one of them is fully controllable. Because the production of noncontrollable RES can not be forecasted perfectly, therefore an optimal dispatch schedule within VPP is needed. To address this problem, an APSO （accelerated particle swarm optimization） is used to solve the constrained optimal dispatch problem within VPP. The experimental results show that the proposed optimization method provides high quality solutions while meeting constraints.

Multi-Layer and Multi-Objective Optimization Design of Supporting Structure of Large-Scale Spherical Solar Concentrator for the Space Solar Power Station

Space solar power station is a novel renewable energy equipment in space to provide the earth with abundant and continuous power.The Orb-shaped Membrane Energy Gathering Array,one of the alternative construction schemes in China,is promising for collecting space sunlight with a large-scale spherical concentrator.Both the structural and optical performances such as root mean square deformation,natural frequency,system mass,and sunlight blocking rate have significant influences on the system property of the concentrator.Considering the comprehensive performance of structure and optic,this paper proposes a novel mesh grid based on normal polyhedron projection and spherical arc bisection for the supporting structure to deal with the challenge of the large-scale structural modular design.For both achieving low system mass and high surface precision,a multilayer and multi-objective optimization model is proposed by classifying the supporting structure into different categories and optimizing their internal and external diameters.The Particle Swarm Optimization algorithm is adopted to find optimal sectional dimensions of the different kinds of supporting structure.The infinite model is also established and structural analysis is carried out,which are expected to provide a certain reference for the subsequent detailed structural design.The numerical results indicate that the spherical concentrator designed by the novel mesh grid would obtain as high as 94.37%sunlight collection efficiency.The supporting structure constructed with the multiple layers would reduce the system quality by 6.92%,sunlight blocking rate by 28.54%,maximum deformation by 41.50%,and root mean square by 9.48%to the traditional single layer,respectively.