Multi-objective optimal reconfiguration and DG (Distributed Generation) power allocation in distribution networks using Big Bang-Big Crunch algorithm considering load uncertainty
In this paper, a multi-objective framework is proposed for simultaneous network reconfiguration and power allocation of DGs (Distributed Generations) in distribution networks. The optimization problem has objective functions of minimizing power losses, operation cost, and pollutant gas emissions as well as maximizing the voltage stability index subject to different power system constraints. The uncertainty of loads is modeled using the TFN (Triangular Fuzzy Number) technique. A novel solution method called MOHBB-BC (Multi-objective Hybrid Big Bang-Big Crunch) is implemented to solve the optimization problem. The MOHBB-BC derives a set of non-dominated Pareto solutions and accumulates them in a retention called Archive. The diversity of Pareto solutions conserved by applying a crowding distance operator and afterwards, the ‘best compromised’ Pareto solution is selected using a fuzzy decision maker. The proposed method is tested on two test systems of 33-bus and 25-bus in different cases including unbalanced three-phase loads. Results obtained from test cases elaborate that the MOHBB-BC results in more diversified Pareto solutions implying a better exploration capability even with a higher fitness. In addition, considering load uncertainty leads to a more realistic solution than deterministic loads but with higher level of power losses.
Interval type-2 fuzzy PID load frequency controller using Big Bang-Big Crunch optimization
This paper proposes an optimization based design methodology of interval type-2 fuzzy PID (IT2FPID) controllers for the load frequency control (LFC) problem. Hitherto, numerous fuzzy logic control structures are proposed as a solution of LFC. However, almost all of these solutions use type-1 fuzzy sets that have a crisp grade of membership. Power systems are large scale complex systems with many different uncertainties. In order to handle these uncertainties, in this study, type-2 fuzzy sets, which have a grade of membership that is fuzzy, have been used. Interval type-2 fuzzy sets are used in the design of a load frequency controller for a four area interconnected power system, which represents a large power system. The Big Bang-Big Crunch (BB-BC) algorithm is applied to tune the scaling factors and the footprint of uncertainty (FOU) membership functions of interval type-2 fuzzy PID (IT2FPID) controllers to minimize frequency deviations of the system against load disturbances. BB-BC is a global optimization algorithm and has a low computational cost, a high convergence speed, and is therefore very efficient when the number of optimization parameters is high as presented in this study. In order to show the benefits of IT2FPID controllers, a comparison to conventional type-1 fuzzy PID (T1FPID) controllers and conventional PID controllers is given for the four-area interconnected power system. The gains of conventional PID and T1FPID controllers are also optimized using the BB-BC algorithm. Simulation results explicitly show that the performance of the proposed optimum IT2FPID load frequency controller is superior compared to the conventional T1FPID and PID controller in terms of overshoot, settling time and robustness against different load disturbances.
Application of the hybrid Big Bang-Big Crunch algorithm to optimal reconfiguration and distributed generation power allocation in distribution systems
In this paper, a multi-objective framework is proposed for simultaneous optimal network reconfiguration and DG (distributed generation) power allocation. The proposed method encompasses objective functions of power losses, voltage stability, DG cost, and greenhouse gas emissions and it is optimized subject to power system operational and technical constraints. In order to solve the optimization problem, the HBB-BC (Hybrid Big Bang-Big Crunch) algorithm as one of the most recent heuristic tools is modified and employed here by introducing a mutation operator to enhance its exploration capability. To resolve the scaling problem of differently-scaled objective functions, a fuzzy membership is used to bring them into a same scale and then, the fuzzy fitness of the final objective function is utilized to measure the satisfaction level of the obtained solution. The proposed method is tested on balanced and unbalanced test systems and its results are comprehensively compared with previous methods considering different scenarios. According to results, the proposed method not only offers an enhanced exploration capability but also has a better converge rate compared with previous methods. In addition, the simultaneous network reconfiguration and DG power allocation leads to a more optimal result than separately doing tasks of reconfiguration and DG power allocation.
CO2 and cost optimization of reinforced concrete frames using a big bang-big crunch algorithm
A hybrid Big Bang-Big Crunch (BB-BC) optimization algorithm is applied to the design of reinforced concrete frames. The objective of the optimization is to minimize the total cost or the CO2 emissions associated with construction of reinforced concrete frames subjected to constraints based on the specifications and guidelines prescribed by the American Concrete Institute (ACI 318-08). Designs are presented for several reinforced concrete frames that minimize the cost and the CO2 emissions associated with construction. In the first frame example, low-cost designs developed using BB-BC optimization are compared to designs developed using a genetic algorithm. In the second set of frame designs, both low-cost designs using BB-BC optimization are compared to designs developed using simulated annealing. The BB-BC algorithm generated designs that reduced the cost and the CO2 emissions of construction for example frames.
neural network differential equation deep learning convolutional neural network convolutional neural software development deep neural network feature selection open source deep neural optical network learning approach geographic information system distributed generation geographic information stochastic differential equation power allocation open source software stochastic differential deep convolutional neural source software deep convolutional named entity recognition loss function human action space telescope personal area network mental health feature learning wireless personal area elastic optical multi-task learning personal area elastic optical network network reconfiguration wireless personal learning problem hubble space telescope learning task software developer high rate water pressure hubble space doubly stochastic open source tool source software development free and open capacity allocation low rate source software project group lasso path protection sparse learning backup path rate wireles open source gi spare capacity optimal reconfiguration source gi multi-task deep backward doubly stochastic doubly stochastic differential backward doubly big bang-big crunch low rate wireles survivable routing multi-task deep learning guidance sensor bang-big crunch big bang-big multi-task learning approach source software tool deep multi-task deep multi-task learning pose-invariant face high water pressure fine guidance shared backup fine guidance sensor multi-task convolutional neural multi-task learning model source gis software shared backup path multi-task feature bang-big crunch algorithm backup path protection approximating optimal crunch algorithm multi-task learning method software movement multi-task network spare capacity allocation multi-task convolutional multi-task cnn multi-task sparse multi-task feature learning multi-task regression multi-task allocation feature learning algorithm multiple related task multi-task multi-view multi-task learning algorithm soils contaminated