Simultaneous Placement of Distributed Generation and Reconfiguration in Distribution Networks Using Unified Particle Swarm Optimization

The power distribution feeder reconfiguration and optimum placement of distributed generation are two main methods to minimize the active power loss in radial distribution systems. The robustness of the radial distribution system can be improved by simultaneous manipulation of both optimal DG placement and feeder reconfiguration. In this paper, a novel technique is proposed to minimize the power loss with the simultaneous use of feeder reconfiguration and placement of distributed generation. In general, an electrical power network economics primarily relies on the conductor line losses. Hence in this proposed study, the feeder reconfiguration and finding of desirable bus location and operating power of distributed generation is concurrently modeled as an optimization problem for minimizing the real power loss with subject to all operating equality and inequality constraints. This optimization problem is solved with the guide of unified particle swarm optimization algorithm. The system power loss is handled as the cost function for each particle in a swarm. The proposed method is applied to both IEEE 33-bus and IEEE 69-bus radial distribution systems. The prosperous solutions achieved from the simulation studies manifest that the high level of system loss reduction and desirable bus voltage profile, when analyzed against the system with reconfiguration, and the system with DG

Optimization of Welding Input Parameters Using PSO Technique for Minimizing HAZ Width in GMAW

In order to conceive command systems for welding equipment based on intelligence techniques similar to human thinking; it is better to use artificial intelligence methods, for example: Genetic algorithms and particle swarm optimization. Freshly, this latter has received increased attention in many research fields. This paper discuss the application of particle swarm optimization algorithm to optimize the welding process parameters and obtain a better Width of Head Affected Zone (WHAZ) in the welding machine which is gas metal arc welding. The effect of four main welding variables in the gas metal arc welding process, namely welding speed, welding voltage, nozzle-to-plate distance and wire feed speed on the WHAZ are studied. A source code is developed in MATLAB 8.3 to perform the optimization

A modified bats echolocation-based algorithm for solving constrained optimisation problems

A modified adaptive bats sonar algorithm (MABSA) is presented that utilises the concept of echolocation of a colony of bats to find prey. The proposed algorithm is applied to solve the constrained optimisation problems coupled with penalty function method as constraint handling technique. The performance of the algorithm is verified through rigorous tests with four constrained optimisation benchmark test functions. The acquired results show that the proposed algorithm performs better to find optimum solution in terms of accuracy and convergence speed. The statistical results of MABSA to solve all the test functions also has been compared with the results from several existing algorithms taken from literature on similar test functions. The comparative study has shown that MABSA outperforms other establish algorithms, and thus, it can be an efficient alternative method in the solving constrained optimisation problems

Hybrid harmony search algorithm for continuous optimization problems

Harmony Search (HS) algorithm has been extensively adopted in the literature to address optimization problems in many different fields, such as industrial design, civil engineering, electrical and mechanical engineering problems. In order to ensure its search performance, HS requires extensive tuning of its four parameters control namely harmony memory size (HMS), harmony memory consideration rate (HMCR), pitch adjustment rate (PAR), and bandwidth (BW). However, tuning process is often cumbersome and is problem dependent. Furthermore, there is no one size fits all problems. Additionally, despite many useful works, HS and its variant still suffer from weak exploitation which can lead to poor convergence problem. Addressing these aforementioned issues, this thesis proposes to augment HS with adaptive tuning using Grey Wolf Optimizer (GWO). Meanwhile, to enhance its exploitation, this thesis also proposes to adopt a new variant of the opposition-based learning technique (OBL). Taken together, the proposed hybrid algorithm, called IHS-GWO, aims to address continuous optimization problems. The IHS-GWO is evaluated using two standard benchmarking sets and two real-world optimization problems. The first benchmarking set consists of 24 classical benchmark unimodal and multimodal functions whilst the second benchmark set contains 30 state-of-the-art benchmark functions from the Congress on Evolutionary Computation (CEC). The two real-world optimization problems involved the three-bar truss and spring design. Statistical analysis using Wilcoxon rank-sum and Friedman of IHS-GWO’s results with recent HS variants and other metaheuristic demonstrate superior performance

Design of segmental rotor and non-overlap windings in single-phase fefsm for low torque high speed applications

In this research, a new structure of single-phase field excitation flux switching motor (FEFSM) using segmental rotor structure and non-overlap windings arrangement is proposed in order to overcome the drawbacks of low torque and small power performances due to their longer flux path in the single-phase FEFSM using salient rotor structure and overlap windings arrangement. The objectives of this study are to design, analyse and examine performance of the proposed motor, to optimize the proposed motor for optimal performances, and to develop the proposed motor prototype for experimental performance validation. The design and analyses thru 2Dfinite element analysis (FEA) is conducted using JMAG Designer version 15, while deterministic optimization method is applied in design optimization process. To validate the 2D-FEA results, the motor prototype is developed and tested experimentally. Based on various rotor poles analysis, a combination of 12 pole 6 pole (12S-6P) has been selected as the best design due to their highest torque and power capability of 0.91 Nm and 277.4 W, respectively. Besides, the unbalance armature magnetic flux of the proposed FEFSM using segmental rotor has been resolved by using segmental rotor span refinement. The balanced armature magnetic flux amplitude ratio obtained is 1.002, almost 41.2% reduction from the initial design. In addition, the optimized motor has increased maximum torque and power by 80.25% to 1.65 Nm, and 43.6% to 398.6W, respectively. Moreover, copper loss of the optimized design has decreased by 9.7%%, hence increasing the motor efficiency of 25.3%. Finally, the measured results obtained from the prototype machine has reasonable agreement with FEA results, proving their prospect to be applied for industrial and home appliances

Desain Kontroler Pd-Lqr Dengan Upso Untuk Optimalisasi Pengaturan Crane Anti Ayun

Crane merupakan salah satu alat yang digunakan untuk mengangkat dan memindahkan muatan pada jalur yang telah ditentukan. Pada saat alat ini bergerak memindahkan beban, maka beban akan terayun dengan besar sudut ayun tertentu mengikuti perubahan kecepatan perpindahan crane. Optimalisasi pengaturan diperlukan sehingga alat ini dapat memindahkan beban dengan cepat dengan ayunan.yang sekecil mungkin namun dengan fungsi biaya yang seminimal mungkin. Pada tesis ini dikembangkan kontroler crane anti ayun yang mampu mengoptimalkan fungsi fitnees dan syarat kendala yang dihadapi. Kontroler PDLQR dipilih karena kemampuannya mengatur plant dalam daerah steady state. Algoritma uPSO dipakai untuk mencari matriks Q dan R yang tepat untuk mendesain kontroler PD-LQR yang mampu mengkompromikan syarat fungsi biaya, persentase overshoot, waktu settling dan juga dapat mengurangi ayunan beban dan steady state error yang terjadi. Dari hasil penelitian dan implementasi diperoleh bahwa matriks Q dan R hasil optimasi dengan uPSO akan menghasilkan kontroler PD-LQR yang lebih baik dalam menjaga ayunan beban crane. Sedangkan kontroler PD-LQR type I optimasi uPSO akan menurunkan pemakaian energi kontrol rata-rata dan menjaga ayunan beban semakin kecil. ========================================================================================================= Crane is one of the tools used to lift and move the existing load on a predetermined path. When the crane moved the load, it swinged with certain angle depends on speed change of the moving crane. Therefore optimization of settings required to be able moves cranecranes quickly and with swing as small as possible to load but with a minimum cost function is quite eminent. In this thesis developed anti sway crane controller that is able to optimize the functions and requirements fitnees obstacles encountered. PD-LQR controller is chosen for its ability to regulate plant in steady state area. UPSO algorithm is used to find the matrix Q and R is right for PD-LQR controller design that is capable of compromising the cost function terms, the percentage of overshoot, settling time and also can reduce the load swing and steady state error that occurred. This research implies that optimization of the matrix Q and R with uPSO would produce better PD-LQR controller in order to maintain the crane’s swing load. While PD-LQR controller type I uPSO optimization would reduce the average energy consumption control and maintain smaller swing load

Multi particle swarm optimisation algorithm applied to supervisory power control systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonPower quality problems come in numerous forms (commonly spikes, surges, sags, outages and harmonics) and their resolution can cost from a few hundred to millions of pounds, depending on the size and type of problem experienced by the power network. They are commonly experienced as burnt-out motors, corrupt data on hard drives, unnecessary downtime and increased maintenance costs. In order to minimise such events, the network can be monitored and controlled with a specific control regime to deal with particular faults. This study developed a control and Optimisation system and applied it to the stability of electrical power networks using artificial intelligence techniques. An intelligent controller was designed to control and optimise simulated models for electrical system power stability. Fuzzy logic controller controlled the power generation, while particle swarm Optimisation (PSO) techniques optimised the system’s power quality in normal operation conditions and after faults. Different types of PSO were tested, then a multi-swarm (M-PSO) system was developed to give better Optimisation results in terms of accuracy and convergence speed.. The developed Optimisation algorithm was tested on seven benchmarks and compared to the other types of single PSOs. The developed controller and Optimisation algorithm was applied to power system stability control. Two power electrical network models were used (with two and four generators), controlled by fuzzy logic controllers tuned using the Optimisation algorithm. The system selected the optimal controller parameters automatically for normal and fault conditions during the operation of the power network. Multi objective cost function was used based on minimising the recovery time, overshoot, and steady state error. A supervisory control layer was introduced to detect and diagnose faults then apply the correct controller parameters. Different fault scenarios were used to test the system performance. The results indicate the great potential of the proposed power system stabiliser as a superior tool compared to conventional control systems

M.N.: Unified particle swarm optimization for solving constrained engineering optimization problems

Abstract. We investigate the performance of the recently proposed Unified Particle Swarm Optimization method on constrained engineering optimization problems. For this purpose, a penalty function approach is employed and the algorithm is modified to preserve feasibility of the encountered solutions. The algorithm is illustrated on four well–known engineering problems with promising results. Comparisons with the standard local and global variant of Particle Swarm Optimization are reported and discussed.

Teaching Learning based Optimization Applied to Mechanical Constrained Design Problems

Amidst all the evolutionary optimization algorithms Teaching–Learning-Based Optimization (TLBO) seems to be a promising technique with relatively competitive performances. It outperforms some of the well-known metaheuristics regarding constrained benchmark functions, constrained mechanical design, and continuous non-linear numerical optimization problems. This dissertation presents the application of TLBO to various problems of mechanical engineering. Both constrained and unconstrained optimization has been performed on some manufacturing processes and design problems. Parametric optimization of three non-conventional machining processes namely electro-discharge machining, electrochemical machining and electro-chemical discharge machining, have been carried out and the results are compared with other evolutionary algorithms. Improvement in the existing TLBO algorithm has been incorporated in this dissertation using two schemes namely bit string mutation and replacement of worst solutions with fresh ones. Performance evaluation of these modifications have been presented in this dissertation by solving six optimization problems using original TLBO and proposed modifications. It has been found that better results are achieved in reaching the global optimal values by the use of these modifications. However, the results prefer the use of bit string mutation over scheme of replacing the worst solutions with fresh solutions in addition to the original logic of TLBO. The bit wise mutation and replacement of the worst solutions with fresh ones, proved an added advantage to the existing algorithm. Both these modifications resulted in a steeper convergence rate and finally provided global optimal solutions, and in some cases even better solutions than previously published results

A study of search neighbourhood in the bees algorithm

The Bees Algorithm, a heuristic optimisation procedure that mimics bees foraging behaviour, is becoming more popular among swarm intelligence researchers. The algorithm involves neighbourhood and global search and is able to find promising solutions to complex multimodal optimisation problems. The purpose of neighbourhood search is to intensify the search effort around promising solutions, while global search is to enable avoidance of local optima. Despite numerous studies aimed at enhancing the Bees Algorithm, there have not been many attempts at studying neighbourhood search. This research investigated different kinds of neighbourhoods and their effects on neighbourhood search. First, the adaptive enlargement of the search neighbourhood was proposed. This idea was implemented in the Bees Algorithm and tested on a set of mathematical benchmarks. The modified algorithm was also tested on single objective engineering design problems. The experimental results obtained confirmed that the adaptive enlargement of the search neighbourhood improved the performance of the proposed algorithm. Normally, a symmetrical search neighbourhood is employed in the Bees Algorithm. As opposed to this practice, an asymmetrical search neighbourhood was tried in this work to determine the significance of neighbourhood symmetry. In addition to the mathematical benchmarks, the algorithm with an asymmetrical search neighbourhood was also tested on an engineering design problem. The analysis verified that under certain measurements of asymmetry, the proposed ii algorithm produced a similar performance as that of the Bees Algorithm. For this reason, it was concluded that users were free to employ either a symmetrical or an asymmetrical search neighbourhood in the Bees Algorithm. Finally, the combination of adaptive enlargement and reduction of the search neighbourhood was presented. In addition to the above mathematical benchmarks and engineering design problems, a multi-objective design optimisation exercise with constraints was selected to demonstrate the performance of the modified algorithm. The experimental results obtained showed that this combination was beneficial to the proposed algorithm.EThOS - Electronic Theses Online ServiceGBUnited Kingdo