Intelligent Fault Analysis in Electrical Power Grids

Power grids are one of the most important components of infrastructure in today's world. Every nation is dependent on the security and stability of its own power grid to provide electricity to the households and industries. A malfunction of even a small part of a power grid can cause loss of productivity, revenue and in some cases even life. Thus, it is imperative to design a system which can detect the health of the power grid and take protective measures accordingly even before a serious anomaly takes place. To achieve this objective, we have set out to create an artificially intelligent system which can analyze the grid information at any given time and determine the health of the grid through the usage of sophisticated formal models and novel machine learning techniques like recurrent neural networks. Our system simulates grid conditions including stimuli like faults, generator output fluctuations, load fluctuations using Siemens PSS/E software and this data is trained using various classifiers like SVM, LSTM and subsequently tested. The results are excellent with our methods giving very high accuracy for the data. This model can easily be scaled to handle larger and more complex grid architectures.Comment: In proceedings of the 29th IEEE International Conference on Tools with Artificial Intelligence (ICTAI) 2017 (full paper); 6 pages; 13 figure

Scenario-based Economic Dispatch with Uncertain Demand Response

This paper introduces a new computational framework to account for uncertainties in day-ahead electricity market clearing process in the presence of demand response providers. A central challenge when dealing with many demand response providers is the uncertainty of its realization. In this paper, a new economic dispatch framework that is based on the recent theoretical development of the scenario approach is introduced. By removing samples from a finite uncertainty set, this approach improves dispatch performance while guaranteeing a quantifiable risk level with respect to the probability of violating the constraints. The theoretical bound on the level of risk is shown to be a function of the number of scenarios removed. This is appealing to the system operator for the following reasons: (1) the improvement of performance comes at the cost of a quantifiable level of violation probability in the constraints; (2) the violation upper bound does not depend on the probability distribution assumption of the uncertainty in demand response. Numerical simulations on (1) 3-bus and (2) IEEE 14-bus system (3) IEEE 118-bus system suggest that this approach could be a promising alternative in future electricity markets with multiple demand response providers

Genetic algorithms for scheduling generation and maintenance in power systems

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Contingency-Constrained Unit Commitment with Post-Contingency Corrective Recourse

We consider the problem of minimizing costs in the generation unit commitment problem, a cornerstone in electric power system operations, while enforcing an N-k-e reliability criterion. This reliability criterion is a generalization of the well-known $N$-$k$ criterion, and dictates that at least $(1-e_ j)$ fraction of the total system demand must be met following the failures of $k$ or fewer system components. We refer to this problem as the Contingency-Constrained Unit Commitment problem, or CCUC. We present a mixed-integer programming formulation of the CCUC that accounts for both transmission and generation element failures. We propose novel cutting plane algorithms that avoid the need to explicitly consider an exponential number of contingencies. Computational studies are performed on several IEEE test systems and a simplified model of the Western US interconnection network, which demonstrate the effectiveness of our proposed methods relative to current state-of-the-art

A Linear Programming-Driven MCDM Approach for Multi-Objective Economic Dispatch in Smart Grids

This paper presents a novel approach to deal with the multi-objective economic dispatch problem in smart grids as a multi-criteria decision making (MCDM) problem, whose decision alternatives are dynamically generated. Four objectives are considered: emissions, energy cost, distance of supply, and load balancing. Objectives are preliminarily preference-ranked through a fuzzy version of the analytic hierarchy process (AHP), and then classified into two categories of importance. The more important objectives form the objective function of a linear programming (LP) problem, whose solution (driving solution) drives the generation of Pareto-optimal alternative configurations of power output of the generators. The technique for order of preference by similarity to ideal solution (TOPSIS) is used to automatically select the most suitable power output configuration, according to initial preferences, derived with fuzzy AHP. The effectiveness of our approach is validated by comparing it to the weighted sum (WS) method, by simulating 40 different operating scenarios on a prototype smart microgrid