Hybrid Signal Processing and Soft Computing approaches to Power System Frequency Estimation

Dynamic variation in power system frequency is required to be estimated for implementing the correcting measures. This paper presents power system frequency estimation by using RLS-Adaline and KF-Adaline algorithms. In the proposed hybrid approaches the weights of the Adaline are updated using RLS/KF algorithms. Frequency of power system signal is estimated from final updated weights of the Adaline. The performances of the proposed algorithms are studied through simulations for several critical cases that often arise in a power system. These studies show that the KF-Adaline algorithm is superior over the RLS-Adaline in estimating power system frequency. Studies made on experimental data also support the superiority

Forecasting Global Solar Insolation Using the Ensemble Kalman Filter Based Clearness Index Model

This paper describes a novel approach in developing a model for forecasting of global insolation on a horizontal plane. In the proposed forecasting model, constraints, such as latitude and whole precipitable water content in vertical column of that location, are used. These parameters can be easily measurable with a global positioning system (GPS). The earlier model was developed by using the above datasets generated from different locations in India. The model has been verified by calculating theoretical global insolation for different sites covering east, west, north, south and the central region with the measured values from the same locations. The model has also been validated on a region, from which data was not used during the development of the model. In the model, clearness index coefficients (KT) are updated using the ensemble Kalman filter (EnKF) algorithm. The forecasting efficacies using the KT model and EnKF algorithm have also been verified by comparing two popular algorithms, namely the recursive least square (RLS) and Kalman filter (KF) algorithms. The minimum mean absolute percentage error (MAPE), mean square error (MSE) and correlation coefficient (R) value obtained in global solar insolation estimations using EnKF in one of the locations are 2.4%, 0.0285 and 0.9866 respectively

Multiobjective Optimized Smart Charge Controller for Electric Vehicle Applications

The continuous deployment of distributed energy sources and the increase in the adoption of electric vehicles (EVs) require smart charging algorithms. The existing EV chargers offer limited flexibility and controllability and do not fully consider factors (such as EV user waiting time and the length of next trip) as well as the potential opportunities and financial benefits from using EVs to support the grid, charge from renewable energy, and deal with the negative impacts of intermittent renewable generation. The lack of adequate smart EV charging may result in high battery degradation, violation of grid control statutory limits, high greenhouse emissions, and high charging cost. In this article, a neuro-fuzzy particle swarm optimization (PSO)-based novel and advanced smart charge controller is proposed, which considers user requirements, energy tariff, grid condition (e.g., voltage or frequency), renewable (photovoltaic) output, and battery state of health. A rule-based fuzzy controller becomes complex as the number of inputs to the controller increases. In addition, it becomes difficult to achieve an optimum operation due to the conflicting nature of control requirements. To optimize the controller response, the PSO technique is proposed to provide a global optimum solution based on a predefined cost function, and to address the implementation complexity, PSO is combined with a neural network. The proposed neuro-fuzzy PSO control algorithm meets EV user requirements, works within technical constraints, and is simple to implement in real time (and requires less processing time). Simulation using MATLAB and experimental results using a dSPACE digital real-time emulator are presented to demonstrate the effectiveness of the proposed controller

Online Sensorless Solar Power Forecasting for Microgrid Control and Automation

Meteorological conditions such as air density, temperature, solar radiation etc. strongly affect the power generation from solar, and thus, the prediction and estimation process should consider weather conditions as critical inputs. The nature of weather forecast is highly unpredictable, so many applications use meteorological data from in-place on-site sensors to add to the forecast and some use complex networks with complicated mapping. The in-situ sensor approach and dense mapping methods, however, present several drawbacks. First, the use of sensors give rise to extra operational, installation and maintenance cost. Second, it requires significant amount of time to capture and accumulate data for various occasions and scenarios, and in addition, sensor itself can be the cause of error measurements. The complex methods are computational inefficient and may present suboptimal convergence. This paper presents a sensorless solar output power forecasting based on historical weather (publicly available from met office) and PV data. The algorithm uses simple to implement neural networks with few neurons and hidden layers for its training and allows for day a head forecast. The proposed methodology presents a guideline on how to select the relevant data from weather and how it affects the accuracy and training time of neural network. The benefit of developed method is an improvement on the energy management, utilization and reliability of the microgrid

Managing aging in nuclear power plants: Insights from NRC`s Maintenance Team Inspection reports

A plant`s maintenance program is the principal vehicle through which age-related degradation is managed. From 1988 to 1991, the NRC evaluated the maintenance program of every nuclear power plant in the United States. Forty-four out of a total of sixty-seven of the reports issued on these in-depth team inspections have been reviewed for insights into the strengths and weaknesses of the programs as related to the need to understand and manage the effects of aging on nuclear plant structures, systems, and components (SSCs). Relevant information has been extracted from these inspection reports sorted into several categories; including Specific Aging Insights, Preventive Maintenance, Predictive Maintenance and Condition Monitoring, Post Maintenance Testing, Failure Trending, Root Cause Analysis and Usage of Probabilistic Risk Assessment in the Maintenance Process. Specific examples of inspection and monitoring techniques successfully used by utilities to detect degradation due to aging have been identified

Life testing of a low voltage air circuit breaker

A DS-416 low voltage air circuit breaker manufactured by Westinghouse was mechanically cycled to identify age-related degradation in the various breaker subcomponents, specifically the power-operated mechanism. This accelerated aging test was performed on one breaker unit for over 36,000 cycles. Three separate pole shafts, one with a 60-degree weld, one with a 120-degree weld, and one with a 180-degree weld in the third pole lever were used to characterize cracking in the welds. In addition, during the testing three different operating mechanisms and several other parts were replaced as they became inoperable. Among the seven welds on the pole shaft, {number_sign}1 and {number_sign}3 were found to be critical ones whose fracture can result in misalignment of the pole levers. This can lead to problems with the operating mechanism, including the burning of coils, excessive wear in certain parts, and overstressed linkages. Furthermore, the limiting service life of a number of subcomponents of the power-operated mechanism, including the operating mechanism itself, were assessed. Based on these findings, suggestions are provided to alleviate the age-related degradation that could occur as a result of normal closing and opening of the breaker contacts during its service life. Also, cause and effect analyses of various age-related degradation in various breaker parts are discussed

Heuristic Multi-Agent Control for Energy Management of Microgrids with Distributed Energy Sources

The increased integration of distributed Renewable Energy Sources (RESs) and adoption of Electric Vehicles (EVs) require appropriate control and management of energy sources and EV charging. This becomes critical at the distribution system level, especially at a microgrid (MG) level. This control is required not only to mitigate the negative impacts of intermittent generation from RESs but also to make better use of available energy, reduce carbon footprint, maximize the overall profit of microgrid and increase energy autonomy by effective utilization of battery storage. This paper proposes a heuristic multi-agent based decentralized energy management approach for grid-connected MG. The MG comprises of active (controlled) and passive (uncontrolled) electrical loads, a photovoltaic (PV) system, battery energy storage system (BESS) and a charging post for electric vehicles. The proposed approach is aimed at optimizing the use of local energy generation from photovoltaic and smart energy utilization to serve electrical loads and EV as well as maximizing MG profit. The aim of the energy management is to supply local consumption at minimum cost and less dependency on the main grid supply. Utilizing energy available from RESs (PV and BESS), customers satisfaction (fulfilling local demand), considering uncertainty of renewable generation and load consumption and also taking into account technical constraint are the main strengths of the presented framework. Performance of the proposed algorithm is investigated under different operating conditions and its efficacy is verified

An Insight in to Paget’s Disease of Bone

Paget’s disease of bone (PDB) is a common disorder which may affect one or many bones. Although many patients are asymptomatic, a variety of symptoms and complications may occur. PDB is a focal disorder of bone turnover characterized by excessive bone resorption coupled with bone formation. PDB begins with a period of increased osteoclastic activity and bone resorption, followed by increased osteoblast production of woven bone that is poorly mineralized. In the final phase of the disease process, dense cortical and trabecular bone deposition predominates, but the bone is sclerotic and poorly organized and lacks the structural integrity and strength of normal bone. This article briefly reviews the etiopathogenesis, clinical radiographic and histological features of Paget’s disease.Keywords: Osteoclast, osteoprotegerin, receptor activator of NF‑kB, receptor activator of NF‑kB ligan

Cohesive Motion Control Algorithm for Formation of Multiple Autonomous Agents

This paper presents a motion control strategy for a rigid and constraint consistent formation that can be modeled by a directed graph whose each vertex represents individual agent kinematics and each of directed edges represents distance constraints maintained by an agent, called follower, to its neighbouring agent. A rigid and constraint consistent graph is called persistent graph. A persistent graph is minimally persistent if it is persistent, and no edge can be removed without losing its persistence. An acyclic (free of cycles in its sensing pattern) minimally persistent graph of Leader-Follower structure has been considered here which can be constructed from an initial Leader-Follower seed (initial graph with two vertices, one is Leader and another one is First Follower and one edge in between them is directed towards Leader) by Henneberg sequence (a procedure of growing a graph) containing only vertex additions. A set of nonlinear optimization-based decentralized control laws for mobile autonomous point agents in two dimensional plane have been proposed. An infinitesimal deviation in formation shape created continuous motion of Leader is compensated by corresponding continuous motion of other agents fulfilling the shortest path criteria