Optimal Demand Reconfiguration in Three-Phase Distribution Grids Using an MI-Convex Model

The problem of the optimal load redistribution in electrical three-phase medium-voltage grids is addressed in this research from the point of view of mixed-integer convex optimization. The mathematical formulation of the load redistribution problem is developed in terminals of the distribution node by accumulating all active and reactive power loads per phase. These loads are used to propose an objective function in terms of minimization of the average unbalanced (asymmetry) grade of the network with respect to the ideal mean consumption per-phase. The objective function is defined as the l1-norm which is a convex function. As the constraints consider the binary nature of the decision variable, each node is conformed by a 3×3 matrix where each row and column have to sum 1, and two equations associated with the load redistribution at each phase for each of the network nodes. Numerical results demonstrate the efficiency of the proposed mixed-integer convex model to equilibrate the power consumption per phase in regards with the ideal value in three different test feeders, which are composed of 4, 15, and 37 buses, respectively.

Soil-structure interaction analysis in reinforced concrete structures on footing foundation

Soil-structure interaction (SSI) evaluates how soil or rock deformability imposes on the structure a different load path in a hypothesis of fixed supports, altering the loads acting on the structural elements and the ground. This paper discusses the results of the SSI effects in two buildings with a reinforced concrete structure and shallow foundations in a rock mass. The settlements were monitored by field instrumentation in five stages of their construction, making it possible to estimate through interpolation curves the settlements values of some points. The numerical modeling and structural analysis of the buildings were obtained for two different cases of soil-structure interaction. The structure was considered having fixed supports (non-displaceable) and displaceable supports (with stiffness spring coefficients K). The results reveals the occurrence of SSI effects, with a load redistribution between the columns that occurred differently for the different construction stages. Structural modeling proved to be quite representative, pointing to higher vertical load values than the average values present in building edge zones, which contradicts the conventional idea that central columns are more loaded than the edge columns. The soil-structure interaction analyses resulted in different behaviors regarding both towers; pointing out that low settlements and building symmetry in plan minimize the effects of interaction, with no tendency of load redistribution between columns as the structure rigidity increases, as construction development.

Toward fault tolerant modelling for SCADA based electricity distribution networks, machine learning approach

Maintaining electrical energy is a crucial issue, especially in developing countries with very limited possibilities and recourses. However, the increasing reliance on electrical appliances generates many challenges for operators to fix any fault optimally within minimum time. Even with numerous researches conducted in this area, very few were interested in minimizing the fault duration, especially in the developing countries with very limited resources. Since decision-making requires enough information within minimum time, the integration of information technology with the existing electrical grids is the most appropriate. In this paper, we propose precise and accurate load redistribution estimation models. While several modeling techniques exist, the proposed modeling techniques in this work are based on machine learning models: multiple linear regression, nonlinear regression, and classifier neural network models. The novelty of this work is it introduces a fault-tolerant approach that relies on machine learning and supervisory control and data acquisition system (SCADA). The purpose of this approach is to help electricity distribution companies to maintain power for the customers and to shorten the fault duration from many hours to the minimum possible time. The work was performed based on real data of smart grids split into zones of about 20 transformers. The models’ input data collected from the sensors allocated in the power grid, make the grid becomes able to redistribute the loads by sufficient strategies. To test and validate the models, two powerful modeling tools were used: MATLAB and Anaconda–Python. The results showed an accuracy of about 97% with a standard deviation of 2.3%. The load redistribution was also presented in details. With such eager results, they approve the validity of our model in minimizing the fault duration, by helping the system in taking ideal actions within the optimal time.