Improved evolutionary programming with dynamic mutation and metropolis criteria for multi-objective reactive power optimisation

In the distribution network containing distributed generators (DG), both DG and capacitors can be adjusted for reactive power, and there is a strong complementarity between the two. If the reactive power compensation ability of each grid-connected DG can be fully utilized, it will effectively reduce the voltage fluctuation and the number of equipment actions, which will help to improve the operation level of the distribution network. In this paper reactive power optimization of distribution networks containing distributed power sources has studies. An improved evolutionary programming (EP) method is proposed to solve the optimization goal, using a variable step-size evolution method and optimization strategy with small population and high convergence accuracy. The algorithm improves the random dynamic step method. In different stages of optimization, different step sizes are used to improve the optimization effect. At the same time, considering that some wind farms are equipped with AVC and some wind farms are equipped with capacitor banks, the evolutionary operator is improved. The operator evolves directly on the integer, so it is easier to find the global optimal solution. This paper has used IEEE33 bus system to verification. Results show that the DG can reduce active power loss in distribution network and ameliorate the voltage level. Meanwhile, the improved evolutionary programming method has good optimization effect, fast convergence speed and high search efficiency.

The perfomance of neighborhood searching of GA with general mutation operator is not satisfiable. In the paper, an improved mutation operator is presented. After using GA with an improved mutation operator, the model of optimization of delivery route with single distribution center and simglevehicle is established. Standard test data are used for simulation. The proportional selection operator, tournament selection operator and truncation selection operator in GA are compared, and the result is that by using truncation selection operator, a better optimal effect can be obtained. A dynamic switching mutation operator is also proposed. It is based on 3-opt mutation operator, sub-router exchange mutation operator and two point exchange mutation operator. Simulation results show that the dynamic switching mutation operator can enhance the ability of the neighborhood searching, and GA using dynamic switching mutation operator can get a relatively stable result.

With the development of science and technology, there has been growing the grid size, the growing demand for electricity and the extent of increasing the electricity market.In this paper, a mathematical morphology and genetic algorithm based dynamic reactive power optimization method for distribution network is proposed.By means of constructing mathematical morphology filter, the binary image composed by allelic genes of chromosome is filtered and the problem that deals with the action times of voltage regulating devices for compensation is turned into filtering problem of discrete binary image.With further automate scheduling, distribution automation and unmanned substation reactive power optimization of distribution line running made an urgent request.Reactive power optimization can improve the voltage passing rate, increasing the reliability of the system, the power quality, and the system runs the security and economy of the perfect combination, very broad application prospects.

Evolutionary programming method for minimizing the number of tardy jobs

Scheduling algorithm based on evolutionary computing in identical parallel machine production line

Performance improvement of self-adaptive evolutionary methods with a dynamic lower bound

A hybrid evolutionary programming (EP) method is presented for global optimization of complex circuits. The conventional EP is integrated with a clustering algorithm to improve the robustness of the algorithm for complex multimodal circuit optimization problems. The EP generates populations around the regions of the search space which can potentially contain a minimum but may be overlooked. The clustering algorithm is used to identify these regions dynamically. In order to improve the speed of optimization, the EP is combined with a gradient-based search method in an efficient fashion. The local search is performed from the center of each identified cluster in order to find the minimum in the region very fast. The hybrid algorithm can also reduce the search space by avoiding the search in the areas that were previously investigated. This feature greatly improves the speed of optimization and prevents the premature convergence as well. The algorithm performed very well in several benchmark problems including a test function minimization and global optimization of a complex RF diplexer circuit.

In this paper, hybrid tabu search and simulated annealing (TSSA) with search space managing methods are proposed to determine the optimal number and allocation of FACTS controllers to enhance power transfer capability of power transactions between generators and loads in power systems. Particular optimal allocation includes optimal locations and parameter settings. Two types of FACTS controllers including thyristor-controlled series capacitor (TCSC) and static var compensator (SVC) are used individually. The objective function is formulated as maximizing total transfer capability (TTC) and minimizing power losses. Power transfer capability determinations are calculated based on the optimal power flow (OPF) technique. Split and non-split search space managing methods are used to improve the solution searching capability. Test results on IEEE 118-bus system and the practical Electricity Generating Authority of Thailand (EGAT) 58-bus system show that the proposed hybrid TSSA with optimal number of FACTS criteria and the split search space managing method give higher TTC and less number of FACTS controllers than those from evolutionary programming (EP) and non-split search space method.

Chee-Kiong SOH 1 & Yaowen YANG 2 ABSTRACT The electro-mechanical impedance (EMI) method is a relatively new nondestructive evaluation (NDE) method which uses the electro-mechanical coupling effect of piezoceramic Lead Zirconate Titanate (PZT) transducers to measure the force impedance of the structure. Due to the electro-mechanical coupling effect of the transducers, any changes in the mechanical impedance of the structure caused by the damage will affect the electrical impedance of the transducer. By comparing the impedance spectra of the damaged structure with the baseline, i.e., the impedance spectra for the pristine structure, the damage in the structure can be assessed. The quantity of the damage is assessed by a statistically calculated damage index, such as root mean square deviation or mean absolute percentage deviation or covariance change or correlation coefficient deviation. The EMI method has shown some advantages over the conventional NDE methods. However, it is not able to simultaneously identify the location and quantify the damage. This paper presents a technique integrating evolutionary programming and the EMI method to overcome this limitation. Essentially, the proposed technique first makes use of the EMI method to measure the variations of the electro-mechanical impedance of the structure. The damage is then identified by a system identification technique which is generally employed in the vibration-based method. Due to the numerous local optima in the search space, the traditional optimization strategies may not be able to find the correct solution. Evolutionary programming (EP) is then used as the system identification technique to find the global optimum. Thus, both the location and the quantity of the damage can be simultaneously identified. In order to enhance the integrated EP and EMI method, a fitness function, which can be generally applied to the other methods, is proposed to discriminate the variations caused by damages from the discrepancies caused by modelling errors. An experimental test is carried out on a cylindrical shell specimen to verify the damage detection results. The experiment demonstrated that both the location and the extent of damage can be simultaneously identified by the integrated EP-EMI method.

This paper presents a new approach to solving the short-term unit commitment problem in utility system using an evolutionary programming based tabu search method. The objective of this paper is to find the generation scheduling such that the total operating cost can be minimized, when subjected to a variety of constraints. This also means that it is desirable to find the optimal generating unit commitment in the power system for the next H hours. Evolutionary programming, which happens to be a global optimization technique for solving unit commitment problem, operates on a system, which is designed to encode each unit's operating schedule with regard to its minimum up/down time. The best population is selected by evolutionary strategy. A 66-bus utility power system with twelve generating units in India demonstrates the effectiveness of the proposed approach. Numerical results are shown comparing the cost solutions and computation time obtained by using the evolutionary programming method and other conventional methods like dynamic programming, Lagrangian relaxation and tabu search in reaching proper unit commitment.

In this paper, a novel and efficient real-coded genetic algorithm (RCGA) for process optimization is developed. The proposed RCGA is equipped with Ranking Selection (RS), Direction-Based Crossover (DBX) and Dynamic Random Mutation (DRM) operators. The RS operator is used to eliminate the bad solutions and reproduce good solutions, making the whole population to achieve a better average fitness. The DBX operator uses relative fitness information to direct the crossover toward a direction that significantly improves the objective fitness. The DRM operator prevents the premature convergence of RCGA and at the same time increases the precision of the searched solution. The effectiveness and application of the proposed RCGA are demonstrated through a variety of single objective optimization benchmark problems. For comparative study, other existing RCGAs with different evolution operators are also performed to the same problem set. Extensive experiment results reveal that the proposed RCGA provides a significantly faster convergence speed and much better search performance than comparative methods.

The large-scale integration of distributed renewable generation (DRG) reduces carbon emissions in power systems, but its intermittency and volatility introduce new challenges for voltage and frequency control. This paper proposes a multi-time-scale reactive power optimization approach that incorporates demand-side response. Firstly, based on the interaction characteristics between sources and loads in the distribution network, a demand-side response mechanism using dynamic time-of-use pricing is designed to explore the source-load matching and tracking capabilities. Secondly, a multi-scale day-ahead reactive power optimization control model is constructed, which comprehensively considers the dynamic response mechanism on the demand side. The objective function of this model aims to minimize network losses and voltage deviations while accounting for the day-ahead price response characteristics of loads. The multi-scale reactive power optimization model is then transformed into a Markov decision process and solved via a locally-observed multi-agent deep deterministic policy gradient algorithm to obtain real-time intraday reactive power and voltage control strategies. Finally, the effectiveness of the proposed method is verified through experiments on the IEEE 33-bus system and an actual power grid. Experimental results show that this method can effectively coordinate various reactive power compensation devices and flexible resources, and improve the efficiency and real-time performance of reactive power and voltage control.

Distribution network planning and operation require the identification of the best topological configuration that is able to fulfill the power demand with minimum power loss. This paper presents an efficient hybridization of both Particle Swarm Optimization (PSO) and Evolutionary Programming (EP) methods which is called the Evolutionary Particle Swarm Optimization (EPSO). The proposed method is used to find the optimal network reconfiguration and optimal size of Distribution Generation (DG) simultaneously. The main objective of this paper is to gain the lowest result of real power losses in the distribution network while improve the computational time as well as satisfying other operating constraints. A comprehensive performance analysis is carried out on IEEE 33 bus distribution system. The proposed method is applied and its impact on the network reconfiguration for real power loss is investigated. The results are presented and compared with the strategy of separated DG sizing and network reconfiguration.

Evolutionary Programming (EP) and Differential Evolution (DE) are well known as simple and efficient schemes for global optimization over continuous spaces. Both EP and DE use mutation for producing offspring. The mutation operators of EP usually generate the search step size for mutation by probability distribution functions while the mutation operators of DE generate it by adding a weighted difference vector between two individuals to a third individual. In this paper, a new EP algorithm is proposed based on dual mutation strategy (DMEP) as it incorporates both the mutation operators of EP and DE literature. Thus the balance between exploration and exploitation is obtained by two different categories of mutation operators. To evaluate the performance of the proposed scheme, a test-suite of 37 benchmark functions has been used and results have been compared with some prominent evolutionary systems. Experimental results show the remarkable effectiveness of the dual mutation strategy employed by DMEP.

Conventional implementation of evolutionary programming (EP) for continuous parameter optimization uses Gaussian mutations. However, an implementation of EP with Cauchy mutation operator has empirically been shown to outperform EP using the Gaussian mutations for optimizations of multi-modal functions with many local optima. The faster convergence of EP with Cauchy mutation has been explained in terms of the fatter tails of the Cauchy probability density function, which results in a higher probability of escaping from a local optimum. In this work we present an implementation of EP consisting of a hybrid linear combination of the Cauchy and Gaussian mutations for antenna optimization problems in order to exploit the desirable properties of these two operators. The implementation follows a procedure similar to the one described by Chellapilla (see IEEE Trans. on Evolutionary Computation, September 1998) for function optimization and uses a self-adaptive scheme for updating the standard deviation and the scale parameter of Gaussian and Cauchy distributions, respectively, during the evolution. As an example the optimization of a six-element Yagi-Uda array of dipoles using the hybrid method is presented.