A Line Flow Granular Computing Approach for Economic Dispatch with Line Constraints

© 2017 IEEE. Line flow calculation plays a critically important role to guarantee the stable operation of power system in economic dispatch (ED) problems with line constraints. This paper presents a line flow granular computing approach for power flow calculation to assist the investigation on ED with line constraints, where the hierarchy method is adopted to divide the power network into multiple layers to reduce computational complexity. Each layer contains granules for granular computing, and the layer network is reduced by Ward equivalent retaining the PV nodes and boundary nodes of tie lines to decrease the data dimension. Then, Newton-Raphson method is applied further to calculate the power line flows within the layer. This approach is tested on IEEE 39-bus and 118-bus systems. The testing results show that the granular computing approach can solve the line flow problem in 9.2 s for the IEEE 118-bus system, while the conventional AC method needs 44.56 s. The maximum relative error of the granular computing approach in line flow tests is only 0.43%, which is quite small and acceptable. Therefore, the case studies demonstrate that the proposed granular computing approach is correct, effective, and can ensure the accuracy and efficiency of power line flow calculation

Binary Classification of Multigranulation Searching Algorithm Based on Probabilistic Decision

Multigranulation computing, which adequately embodies the model of human intelligence in process of solving complex problems, is aimed at decomposing the complex problem into many subproblems in different granularity spaces, and then the subproblems will be solved and synthesized for obtaining the solution of original problem. In this paper, an efficient binary classification of multigranulation searching algorithm which has optimal-mathematical expectation of classification times for classifying the objects of the whole domain is established. And it can solve the binary classification problems based on both multigranulation computing mechanism and probability statistic principle, such as the blood analysis case. Given the binary classifier, the negative sample ratio, and the total number of objects in domain, this model can search the minimum mathematical expectation of classification times and the optimal classification granularity spaces for mining all the negative samples. And the experimental results demonstrate that, with the granules divided into many subgranules, the efficiency of the proposed method gradually increases and tends to be stable. In addition, the complexity for solving problem is extremely reduced

Investigating the applicability of Bayesian networks to demand forecasting during the final phase of support operations

A challenge faced by businesses that provide logistical support to systems is when the provision of those support services is no longer required. A typical example of such a situation is when military operations come to an end. In such cases, those companies that have a contract with the Armed Forces to provide maintenance support for the deployed systems, need to maintain those systems at minimum cost during that final phase, that is from the time the decision to stop the operations is announced until their very end. During the final phase, a challenging problem is forecasting the demand for spare parts, corresponding to equipment failures within the system. This is because the support context, the number of supported systems, the support equipment or even the operational demand can change during that period, and also because there can be very limited opportunities to place orders to cover demand. This thesis suggests that these types of problems can take advantage of the data that have been collected during the support operations prior to the initiation of the closing down process. Moreover, the thesis investigates the exploitation of these data by the use of Bayesian Networks to forecast the demand for spares that will be required for the provision of maintenance during the final phase. The research uses stochastically simulated Support Chain scenarios to generate data and also to evaluate different methods of constructing Bayesian Networks. The simulated scenarios differ in the demand context as well as in the complexity of the Equipment Breakdown Structure of the supported systems. The Bayesian Networks’ structure development methods that are tested include unsupervised machine learning, eliciting the structure from Subject Matter Experts, and two hybrid approaches that combine expert elicitation and machine learning. These models are compared to respective logistic regression models, as well as subject matter experts-adjusted single exponential smoothing forecasts. The comparison of the models is made using both accuracy metrics and accuracy implication metrics. These forecast models’ comparison methods are analysed in order to evaluate their appropriateness. The analyses have provided a number of novel outputs. The algebraic analysis of the accuracy metrics theoretically proves empirical problems that have been discussed in the literature but also reveals others. Regarding the accuracy implication metrics, the analysis shows that for the particular type of problems examined in this thesis –final phase problems – the accuracy implication metrics commonly applied are not enough to inform decision making, and a number of additional ones are required.The research shows that for the scenarios examined, the Bayesian Networks that had their structure learned using an unsupervised algorithm performed better in the accuracy metric than any of the other models. However, even though these Bayesian Networks also did well with the accuracy implication metrics, neither they, nor any of the others was consistently dominant. The reason for the discrepancy in the results between the accuracy and the accuracy implication metrics is that the latter are not only driven by how accurate the forecast model’s prediction is, but also by the model of the residual error and the bias