Moussa Reda Mansour

Node-depth Encoding and Evolutionary Algorithms applied to service restoration in distribution systems

Service restoration in distribution systems is usually formulated as a multi-objective and multi-constrained optimization problem. In order to improve the performance of Evolutionary Algorithms (EAs) applied in such problem, a new tree encoding, called Node-depth Encoding (NDE), has been successfully applied together with both a conventional EA and a modified version of the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II). The objective of this paper is to verify what is the best choice to use to treat service restoration problem in large scale distribution systems? NDE with the conventional EA or NDE with the modified version of the NSGA-II. In order to do that, simulation results in two distribution systems are presented. One of these systems is the fairly large distribution system of Sao Carlos city in Brazil with 3,860 buses, 532 sectors, 509 normally closed sectionalizing switches, 123 normally open tie-switches, 3 substations, and 23 feeders.

Look-ahead based method for selection of preventive control for voltage stability analysis

In recent years, the analysis of voltage stability has become crucial for the proper operation of electrical power systems, especially when it comes to large systems. The need for effective voltage control strategies have been brought to evidence in a considerable number of studies, as power system loads are located farther away from power plants and losses have tended to increase due to heavier power flows over long distance transmission loads. A new methodology for selection of the most effective controls to prevent voltage instability in electrical power systems is developed in this paper. The proposed methodology is fast and suitable for real time operation. It is based on a sensitivity analysis of both a maximum loadability estimate (which is obtained via Look-Ahead method) and the load flow solution with respect to the selected controls. The methodology was successfully tested in two systems: the reduced equivalent model of the southern Brazilian system (represented by a 33-bus model) and the south-southeast Brazilian interconnection (represented by a 65-bus model).

Preventive Control Design for Voltage Stability Considering Multiple Critical Contingencies

A new methodology for selecting a global group of the most effective preventive controls in the context of voltage stability analysis in electrical power systems for a set of multiple critical contingencies is developed in this paper. This methodology is based on a sensitivity analysis of the maximum loadability point with respect to voltage controls. Considering not only the effectiveness of the control elements but also their availability, coordination, and cost, the methodology determines a set of controllers to simultaneously eliminate all critical contingencies. The methodology is fast and suitable for online assessment of preventive control. The methodology was successfully tested in a reduced south-southeast Brazilian system composed of 107 buses.

Identifying groups of preventive controls for a set of critical contingencies in the context of voltage stability

A new methodology for grouping and selecting the most effective controls for a group of critical contingencies to prevent voltage instability in electrical power systems is developed in this paper. This methodology is based on a sensitivity analysis of the maximum loadability point with respect to voltage controls and on a hierarchical clustering analysis. Considering not only the effectiveness of the control elements but also their availability, the methodology design a set of controllers to eliminate all critical contingencies. The methodology is fast and suitable for on-line assessment of preventive control. The methodology was successfully tested in a reduced south-southeast Brazilian system composed of 107 buses.

A New and Fast Method for Preventive Control Selection in Voltage Stability Analysis

A new method for selection of the most effective controls to prevent voltage instability in electrical power systems is developed in this paper. The proposed method is fast and suitable for real time operation. It is based on a sensitivity analysis of both a maximum loadability estimate (which is obtained via Look-Ahead method) and the load flow solution with respect to the selected controls. These sensitivities are calculated without computing the maximum loadability points, which significantly speeds-up the analysis. The method was successfully tested in a reduced model of the south-southeast Brazilian system composed of 107 buses.

Integrating relevant aspects of moeas to solve loss reduction problem in large-scale Distribution Systems

Distribution System (DS) reconfiguration for power loss reduction is usually formulated as a nonlinear, multi-objective and multi-constrained optimization problem. Recently an approach to solve this problem that presents a very good performance even for large-scale DSs was proposed. This approach, called MEAN, combines a multi-objective Evolutionary Algorithm (EA) based on subpopulation tables with a new tree encoding, named Node-Depth Encoding. In order to improve the performance of the MEAN, this paper proposes to incorporate elements from the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) into the MEAN. Basically, the proposed approach stores the non-dominated solutions from Pareto fronts (calculated as the NSGA-II does) into the MEAN by using additional subpopulation tables. The proposed method better explores the space of the objective functions and, consequently, better approximates the Pareto-optimal front. The direct result is the discovery of feasible solutions with significantly lower power losses and also a lower number of switching operations. Simulations results with DSs ranging from 632 to 1,277 switches have shown that with the incorporation of the additional subpopulation tables the performance of the MEAN is significantly improved.

A global group of preventive controls for critical contingencies in the context of voltage stability

A new methodology for grouping and selecting the most effective controls to prevent voltage instability in electrical power systems with multiple critical contingencies is developed in this paper. This methodology is based on a sensitivity analysis of the maximum loadability point with respect to voltage controls. Considering not only the effectiveness of the control elements but also their availability, the methodology design a set of controllers to eliminate all critical contingencies. The methodology is fast and suitable for on-line assessment of preventive control. The methodology was successfully tested in a reduced south-southeast Brazilian system composed of 107 buses.

A tool to group and coordinate preventive controls actions on the context of voltage stability assessment

A new methodology for grouping and adjusting the most effective controls actions to prevent voltage instability in electrical power systems with multiple critical contingencies is developed in this paper. This methodology is based on a sensitivity analysis of the maximum loadability point with respect to voltage controls and a parallel evolutionary algorithm. Considering not only the effectiveness of the control elements but also their availability, the methodology designs a set of controllers to eliminate all critical contingencies. The methodology was successfully tested in a reduced south-southeast Brazilian system with 107 buses and 171 lines.

An automatic procedure for power oscillation dampers design of FACTS devices

In this paper, a procedure is presented to the design of supplementary oscillation controllers installed on FACTS devices. The power system and controllers are modeled in the linearized state-space, and the control problem is formulated through a set of linear and bilinear matrix inequalities, which are solved through the V-K iterations algorithm. The algorithm also presents the possibility of fixing the control structure as a design criteria. Tests were performed in a modified version of the IEEE two area benchmak power system, and include a comparison of fixed and non-fixed structures design, as well as results using an efficient computation through a dedicated computer. The results show that the proposed automatic procedure helps control specialists in enhancing the design of oscillation dampers in a fast computational tool, specially when efficient computing is performed.

Watersheds on Hypergraphs for Data Clustering

We present a novel extension of watershed cuts to hypergraphs, allowing the clustering of data represented as an hypergraph, in the context of data sciences. Contrarily to the methods in the literature, instances of data are not represented as nodes, but as edges of the hypergraph. The properties associated with each instance are used to define nodes and feature vectors associated to the edges. This rich representation is unexplored and leads to a data clustering algorithm that considers the induced topology and data similarity concomitantly. We illustrate the capabilities of our method considering a dataset of movies, demonstrating that knowledge from mathematical morphology can be used beyond image processing, for the visual analytics of network data. More results, the data, and the source code used in this work are available at https://github.com/015988/hypershed.