Generator Fault Diagnosis with Bit-Coding Support Vector Regression Algorithm

Generator fault diagnosis has a great impact on power networks. With the coupling effects, some uncertain factors, and all the complexities of generator design, fault diagnosis is difficult using any theoretical analysis or mathematical model. This paper proposes a bit-coding support vector regression (BSVR) algorithm for turbine generator fault diagnosis (GFD) based on a support vector machine (SVM) capable of processing multiple classification problems of fault diagnosis. The BSVR can simplify the design architecture and reduce the processing time for detection, where m classifier is needed for m class problems compared to the [m(m − 1)]/2 size of the original multi-class SVM. Compared with conventional methods, numerical test results showed a high accuracy, good robustness, and a faster processing performance

A New Model to Calculate Contributions of the Distributed Power

Besides metering, a more transparent load with known power distribution is valuable to draft energy strategies, especially in a deregulated power market. Thus, the usage-of-transmission can be considered properly. There are many studies published on related subjects and every research tried to solve a particular part of the problem. There are three basic categories for discussing power distributions: (i) the real power distribution, (ii) the reactive power distribution, and (iii) the loss allocation. These categories were very often treated separately, with the mutual coupling terms, counter flows, and line charging largely neglected. However, we know that these entities are non-separable. These are inter-related entities; the change of one entity will cause the change to every other entity. A good method should consider these entities altogether, while satisfying all electrical theories. This study developed a method to solve the above problem, with all electrical entities solved, satisfying all electrical circuit theories. With several matrix formulations, this method is capable of solving and tracing all electrical entities, including the current flow, the real and reactive power, the counter flow, and the couplings between the active and reactive power. The algorithm can also allocate power distributions and loss among participants effectively. Besides, a line usage idea is formed to allocate the loss to each generator, where counter flows are not necessarily penalized. It can be awarded sometimes. The idea can integrate with the existent tariffs in a deregulated market

Fast Support Vector Machine for Power Quality Disturbance Classification

The power quality disturbance (PQD) problem involves problems of voltage swell, voltage sag, power interruption, harmonics and complex events involving multiple PQD problems. The PQD problem attracted considerable attention from utilities, especially when renewable energy is getting a higher penetration. The PQD problem could downgrade the service quality, causing problems of malfunctions and instabilities. This paper proposed a simplified SVM technique to identify the PQD problem including the multiple PQD classification. With the simple structure proposed, the methodology could reduce a great deal of training data; requires much less memory space and saves computing time. An IEEE 14-bus power system was used to show the performance. Many tests were conducted, and the method was compared with an artificial neural network (ANN). Simulation results showed the shortened processing time and the effectiveness of the proposed approach

Energy Management Strategy for Microgrids by Using Enhanced Bee Colony Optimization

This paper presents a microgrid (MG) energy management strategy by considering renewable energy and battery storage systems. Renewable energy, including wind power generation and solar power generation, is integrated into the distribution network, for which is formulated the optimal dispatch model of mixed-power generation by considering the charging/discharging scheduling of battery storage systems. The MG system has an electrical link for power exchange between the MG and the utility during different hours of the day. Based on the time-of-use (TOU) and all technical constraints, an enhanced bee colony optimization (EBCO) is proposed to solve the daily economic dispatch of MG systems. In the EBCO procedure, the self-adaption repulsion factor is embedded in the bee swarm of the BCO in order to improve the behavior patterns of each bee swarm and increase its search efficiency and accuracy in high dimensions. Different modifications in moving patterns of EBCO are proposed to search the feasible space more effectively. EBCO is used for economic energy management of grid-connected and stand-alone scenarios, and the results are compared to those in previous algorithms. In either grid-connected or stand-alone scenarios, an optimal MG scheduling dispatch is achieved using micro-turbines, renewable energy and battery storage systems. Results show that the proposed method is feasible, robust and more effective than many previously-developed algorithms

Nonsurgical periodontal treatment and prosthetic rehabilitation of a renal transplant patient with gingival enlargement: a case report with 2-year follow-up

Abstract Background Drug-induced gingival enlargement is a common condition which can be observed in patients taking immunosuppressive medications following organ transplant surgery. The disfiguring excessive tissue often hinders proper oral hygiene practices, therefore accompanied by periodontitis, tooth mobility, and even pathological tooth migration in extreme cases. This case report presents a conservative treatment protocol for a patient with the aforementioned conditions involving neither surgical nor orthodontic intervention. Few related studies have reported such a noninvasive protocol for managing these kinds of conditions. Case presentation A 51-year-old woman presented with bleeding gingiva, mobile teeth and complained of chewing difficulties. She had undergone renal transplant surgery 16 years prior to this dental visit and had been taking immunosuppressive drugs including cyclosporine ever since. After clinical and radiographic examinations, the patient was diagnosed with drug-induced gingival enlargement, pathological tooth migration, severe periodontitis, and missing teeth. Through careful and meticulous nonsurgical debridement, oral hygiene instruction, tooth extraction, and occlusal adjustment, the patient’s periodontium was restored to a healthy state without surgical intervention. Moreover, the patient’s chewing function was restored by means of removable partial dentures. Good adaptation of prostheses and satisfaction with overall treatment outcomes were reported. Conclusions Through proper diagnosis, treatment, and with good patient cooperation, complex systemic and dental problems can be managed conservatively without invasive surgeries to attain a stable periodontium and eventually, occlusal function could be restored

The Feasibility Assessment of Power System Dispatch with Carbon Tax Considerations

Traditional economic dispatch methods, which are used to minimize fuel costs, have become inadequate because they do not consider the environmental impact of emissions in the optimization process. By taking into account the horizon year load and carbon taxes, this paper examines the operation and dispatch of power units in a power system. The objective function, including the cost of fuels and the cost of carbon taxes, is solved by the modified particle swarm optimization with time-varying acceleration coefficient (MPSO-TVAC) method under operational constraints. Based on different load scenarios, the influences of various carbon taxes for the dispatch of units are simulated and analyzed. The efficiency and ability of the proposed MPSO-TVAC method are demonstrated using a real 345KV system. Simulation results indicate that the average annual CO2 emissions are 0.36 kg/kwh, 0.41 kg/kwh, and 0.44 kg/kwh in 2012, 2017 and 2022, respectively. As the capacity of gas-fired plants was increased in 2017 and 2022, the average cost in 2017 and 2022 doubled or tripled compared with the average cost in 2012. Reasonable solutions provide a practical and flexible framework for power sectors to perform feasibility assessments of power system dispatch. They can also be used to assist decision-makers in reaching minimal operation cost goals under the policies for desired emissions

A Multi-Input Power Converter for Hybrid Renewable Energy Generation System

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An enhanced radial basis function network for short-term electricity price forecasting

This paper proposed a price forecasting system for electric market participants to reduce the risk of price volatility. Combining the Radial Basis Function Network (RBFN) and Orthogonal Experimental Design (OED), an Enhanced Radial Basis Function Network (ERBFN) has been proposed for the solving process. The Locational Marginal Price (LMP), system load, transmission flow and temperature of the PJM system were collected and the data clusters were embedded in the Excel Database according to the year, season, workday and weekend. With the OED applied to learning rates in the ERBFN, the forecasting error can be reduced during the training process to improve both accuracy and reliability. This would mean that even the "spikes" could be tracked closely. The Back-propagation Neural Network (BPN), Probability Neural Network (PNN), other algorithms, and the proposed ERBFN were all developed and compared to check the performance. Simulation results demonstrated the effectiveness of the proposed ERBFN to provide quality information in a price volatile environment.Orthogonal Experimental Design (OED) Locational Marginal Price (LMP) Radial Basis Function Network Electricity price forecasting Stochastic Gradient Approach (SGA) Factor analysis

An Optimal Scheduling Dispatch of a Microgrid under Risk Assessment

This paper presents the scheduling dispatch of a microgrid (MG), while considering renewable energy, battery storage systems, and time-of-use price. For the risk evaluation of an MG, the Value-at-Risk (VAR) is calculated by using the Historical Simulation Method (HSM). By considering the various confidence levels of the VAR, a scheduling dispatch model of the MG is formulated to achieve a reasonable trade-off between the risk and cost. An Improved Bee Swarm Optimization (IBSO) is proposed to solve the scheduling dispatch model of the MG. In the IBSO procedure, the Sin-wave Weight Factor (SWF) and Forward-Backward Control Factor (FBCF) are embedded in the bee swarm of the BSO to improve the movement behaviors of each bee, specifically, its search efficiency and accuracy. The effectiveness of the IBSO is demonstrated via a real MG case and the results are compared with other methods. In either a grid-connected scenario or a stand-alone scenario, an optimal scheduling dispatch of MGs is carried out, herein, at various confidence levels of risk. The simulation results provide more information for handling uncertain environments when analyzing the VAR of MGs