Space Reduction Methodology Research Articles

Advanced techniques of power system restoration and practical applications in transmission grids

Optimal restoration of a real-time power system following a disruption is a complex process. In view of that and with increase in frequency and severity of power system outages across the US and their impact on consumers and utilities, North American Electric Reliability Corporation elevated the standard of compliance for power system restoration. While several utilities have proposed solutions addressing the elevated standards based on dynamic programming, they could not address the issues for large-scale power systems and real-time operations including those using steady-state and transient analysis due to the curses of dimensionality. In this paper, we introduce a restoration process based on approximate dynamic programming integrated with solution space reduction methodology. The main theoretical foundations of the methodology and key algorithms used in this restoration process include the knowledge of power system engineering, complex network science, concepts of graph theory, and dynamic programming in operation research. We first use the proposed method to demonstrate an optimal restoration process on the IEEE 118-bus test system and then on a 2000-bus synthetic test system. This method addresses the concerns related to the curses of dimensionality and simplifies the solution space and thus can be applied to various complex real-time operation settings.

Urban Drainage Networks Rehabilitation Using Multi-Objective Model and Search Space Reduction Methodology

The drainage network always needs to adapt to environmental and climatic conditions to provide best quality services. Rehabilitation combining pipes substitution and storm tanks installation appears to be a good solution to overcome this problem. Unfortunately, the calculation time of such a rehabilitation scenario is too elevated for single-objective and multi-objective optimization. In this study, a methodology composed by search space reduction methodology whose purpose is to decrease the number of decision variables of the problem to solve and a multi-objective optimization whose purpose is to optimize the rehabilitation process and represent Pareto fronts as the result of urban drainage networks optimization is proposed. A comparison between different model results for multi-objective optimization is made. To obtain these results, Storm Water Management Model (SWMM) is first connected to a Pseudo Genetic Algorithm (PGA) for the search space reduction and then to a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) for multi-objective optimization. Pareto fronts are designed for investment costs instead of flood damage costs. The methodology is applied to a real network in the city of Medellin in Colombia. The results show that search space reduction methodology provides models with a considerably reduced number of decision variables. The multi-objective optimization shows that the models’ results used after the search space reduction obtain better outcomes than in the complete model in terms of calculation time and optimality of the solutions.

Symmetric Uncertainty Based Search Space Reduction for Fast Face Recognition

Face recognition from a large video database involves more search time. This article proposes a symmetric uncertainty based search space reduction (SUSSR) methodology that facilitates faster face recognition in video, making it viable for real time surveillance and authentication applications. The proposed methodology employs symmetric uncertainty based feature subset selection to obtain significant features. Further, Fuzzy C-Means clustering is applied to restrict the search to nearest possible cluster, thus speeding up the recognition process. Kullback Leibler's divergence based similarity measure is employed to recognize the query face in video by matching the query frame with that of stored features in the database. The proposed search space reduction methodology is tested upon benchmark video face datasets namely FJU, YouTube celebrities and synthetic datasets namely MIT-Dataset-I and MIT-Dataset-II. Experimental results demonstrate the effectiveness of the proposed methodology with a 10 increase in recognition accuracy and 35 reduction in recognition time.

An MILP model for the static transmission expansion planning problem including HVAC/HVDC links, security constraints and power losses with a reduced search space

This paper presents a mixed-integer linear programming (MILP) model to solve the transmission network expansion planning (TNEP) problem, considering HVDC and HVAC proposals with security constraints and the system operation cost. The use of an MILP model guarantees convergence to optimality by using existing classical optimization methods. The operation cost is associated with the cost of transmission losses in the system. In this model, the HVDC lines can be either monopolar or bipolar type. Due to the size and complexity of the problem, a new method to reduce the search space solution is proposed, taking into account line losses, HVDC links, and contingencies. The set of HVDC lines in the proposal may use either line-commutated converters or voltage source converters. The model was implemented using the AMPL modeling language and solved using the CPLEX commercial solver. The Southern Brazilian electrical system was used to demonstrate the effectiveness of the proposed MILP model; tests were performed for cases with and without HVDC links, and with and without the system operation cost. The results show high quality solutions, and high performance when the search space reduction methodology (SSRM) is used. The computational time with the SSRM (the study case with HVDC and operation cost) was reduced by 85.9% with respect to the case that uses the complete search space, and also, the optimal solution was found. The importance of including the operation cost as an objective to be minimized is shown. The cost of losses is reduced by 55.7% when comparing the cases with and without the cost of operation included in the objective function and with HVDC lines as proposals.

A Search Space Reduction Strategy and a Mathematical Model for Multistage Transmission Expansion Planning with $$N-1$$ N - 1 Security Constrains

This paper proposes a linear disjunctive model to solve a multistage transmission expansion planning problem (MTEP) considering $$N - 1$$ security constraints. The use of a disjunctive linear model guarantees finding the optimum solution of the problems using existing classical optimization methods. For large-scale systems, when finding the optimum or even high-quality solutions of the MTEP problem is not possible in polynomial time, a search space reduction methodology (SSRM) is proposed. By using SSRM, it is possible to obtain very high-quality solutions or in most cases the optimum solution of the MTEP problem. The $$N-1$$ security constraint indicates that the transmission system must be expanded in such a way that, despite the outage of a system line (a pre-defined set of contingencies), the system continues to operate properly. The model was implemented using a modelling language for mathematical programming (AMPL) and solved using the CPLEX, which is a commercial solver. The IEEE 24-bus, Colombian 93-bus, and Bolivian 57-bus systems are used to evaluate and show the performance of the proposed mathematical model and the search space reduction strategy.

Optimization of Operation Sequencing in CAPP Using Superhybrid Genetic Algorithms-Simulated Annealing Technique

Computer-aided process planning (CAPP) is an important interface between computer-aided design (CAD) and computer-aided manufacturing (CAM) in computer-integrated manufacturing environment. A problem in traditional CAPP system is that the multiple planning tasks are treated in a linear approach. This leads to an overconstrained overall solution space, and the final solution is normally far from optimal or even nonfeasible. A single sequence of operations may not be the best for all the situations in a changing production environment with multiple objectives such as minimizing number of setups, maximizing machine utilization, and minimizing number of tool changes. In general, the problem has combinatorial characteristics and complex precedence relations, which makes the problem more difficult to solve. The main contribution of this work is to develop an intelligent CAPP system for shop-floor use that can be used by an average operator and to produce globally optimized results. In this paper, the feasible sequences of operations are generated based on the precedence cost matrix (PCM) and reward-penalty matrix (REPMAX) using superhybrid genetic algorithms-simulated annealing technique (S-GENSAT), a hybrid metaheuristic. Also, solution space reduction methodology based on PCM and REPMAX upgrades the procedure to superhybridization. In this work, a number of benchmark case studies are considered to demonstrate the feasibility and robustness of the proposed super-hybrid algorithm. This algorithm performs well on all the test problems, exceeding or matching the solution quality of the results reported in the literature. The main contribution of this work focuses on reducing the optimal cost with a lesser computational time along with generation of more alternate optimal feasible sequences. Also, the proposed S-GENSAT integrates solution space reduction, hybridization, trapping out of local minima, robustness, and convergence; it consistently outperformed both a conventional genetic algorithm and a conventional simulated annealing algorithm.

A search space reduction methodology for data mining in large databases

Given the present need for Customer Relationship and the increased growth of the size of databases, many new approaches to large database clustering and processing have been attempted. In this work, we propose a methodology based on the idea that statistically proven search space reduction is possible in practice. Two clustering models are generated: one corresponding to the full data set and another pertaining to the sampled data set. The resulting empirical distributions were mathematically tested to verify a tight non-linear significant approximation.