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

BanditQ -- No-Regret Learning with Guaranteed Per-User Rewards in Adversarial Environments

Classic online prediction algorithms, such as Hedge, are inherently unfair by design, as they try to play the most rewarding arm as many times as possible while ignoring the sub-optimal arms to achieve sublinear regret. In this paper, we consider a fair online prediction problem in the adversarial setting with hard lower bounds on the rate of accrual of rewards for all arms. By combining elementary queueing theory with online learning, we propose a new online prediction policy, called BanditQ, that achieves the target rate constraints while achieving a regret of $O(T^{3/4})$ in the full-information setting. The design and analysis of BanditQ involve a novel use of the potential function method and are of independent interest

Uric acid levels in chronic kidney disease- a hospital based cross-sectional study in RIMS, Ranchi, Jharkhand

Background: Uric acid is the final end product of purine metabolism and is excreted mainly by proximal tubules of the kidney. Raised uric acid levels may lead to proximal tubular injury, endothelial dysfunction, oxidative stress and intra renal inflammation in patients with normal renal function. Uric acid has been deemed as an independent risk factor for progression of CKD. Aim was to study the uric acid levels in different stages of chronic kidney disease and its association with age, sex and other co-morbidities.Methods: 140 patients of chronic kidney disease admitted in RIMS, Ranchi were included in this study and their serum uric acid level were analyzed. Uric acid level more than 7 mg/dl was considered as hyperuricemia. The study was approved by the Institutional Ethics Committee, Rajendra Institute of Medical Sciences, Ranchi, Jharkhand, India. Informed consent was taken from every patient included in the study.Results: Median age±SD was 55±13.47 years (IQR: 45,65; Range: 19-80). Mean±SD uric acid levels in stage 3 CKD was 4.4±1.9 mg/dl, stage 4 CKD was 6.5±4.1 mg/dl, stage 5 CKD was 8.8±3.1 mg/dl (p<0.05). Females were 31.4% and males were 69.6%. Male to female ratio was 2.2:1. The prevalence of hyperuricemia was 50% in females and 66.6% in males.Conclusions: Hyperuricemia is common among CKD patients and more common among males. Uric acid levels increase with progressive decline in eGFR. Monitoring and follow-up of such patients by may lead to delay in onset and progression of complications of CKD