Search

Recently, renewable energy sources integrated with microgrid (MG) networks have provided safe, secure, and reliable power supply to both utility and industrial purposes. Power quality disturbances (PQDs) seriously affect the performance of MG networks and reduce the lifecycle of numerous sensitive devices in MG networks. Hence, this paper presents a new approach to detect and classify the PQDs using discrete wavelet transform, multiresolution analysis, and optimized‐kernel support vector machine. The obtained unique features from DWT‐MRA are fed to train the well‐known intelligent classifiers. In the optimized‐kernel SVM model, computing power is enhanced for classifying multiple PQ events based on the local density and leave‐one‐out (LOO) algorithm. To get higher separation in feature space, the kernel width of each sample is estimated based on the local density. By using the LOO method, an improved grid search strategy is implemented to get the penalty parameter to achieve satisfactory results. Moreover, a typical MG network is simulated in MATLAB software considering the validation of the proposed technique to address the power quality issues in MG networks, and the results of the proposed method are compared with other conventional ML classifiers. The simulation results confirm that the proposed method is more effective and accurate than other intelligent classifiers. [ABSTRACT FROM AUTHOR]

The generation of combustible gas from anaerobic biomass captivation is a well-known technology. Using gas for direct combustion in household stoves in general producing power from biogas is still quite unusual in most emergent countries. The focus of this paper is to implement the importance of biogas as a substitute energy source. Bio-resources are availably wide-reaching in the appearance of lasting agricultural biomass and wastes which can be deformed into biogas. Cow manure has been used for the production of biogas and the generation of electricity. The basic problem faced during the production of electricity is the production of biogas through anaerobic respiration which is the mixture of methane and other undesirable gases. The scrubber employed to remove the undesirable gases is very expensive and needs regular maintenance. A tap system at the top of the digester is used to remove moisture and a bucket filled with iron sponges is used to remove hydrogen sulfide. In the end, we are left with methane and some inert gases which have no adverse effect on combustion. This purified gas is used to produce electricity which is used to run the load. The benefit of this research is that this method is easily and commonly used by villagers without any hurdles. [ABSTRACT FROM AUTHOR]

An Optimal power flow (OPF) is non‐linear and constrained multi‐objective problem. OPF problems are expensive and evolutionary algorithms (EAs) are computationally complex to obtain uniformly distributed and global Pareto front (PF). Therefore, here, hybrid two‐phase algorithm integrated with parameter less constraint technique is applied to solve OPF problem. Proposed technique combines single and multi‐objective EAs to find better convergence and evenly distributed PF. For the validation and effectiveness of proposed algorithm, various conflicting objective functions are formulated and implemented on IEEE 30 and 300‐bus network. Each case is independently run twenty times. Hyper volume indicator technique is employed to find the best PF, and the best‐compromised solution is obtained by using fuzzy decision‐making technique. Recently, maximum integration of wind and solar power is highly encouraged. Complexity of OPF is increased with the integration of uncertain renewable energy resources. Hence, 30‐bus test system is modified by replacing some conventional generators with the wind and solar generation. Uncertainties in wind, solar and load demand are modelled by appropriate probability distribution functions. Simulation results show that the proposed method can find the near global PF of highly complex problems subject to satisfying all the operational constraints. [ABSTRACT FROM AUTHOR]

The key issue of the voltage-sag propagation on the different types of winding connections for the low voltage transformer in the distribution power network is considered in this study. The issues of voltage sag propagation depend upon the type of the asymmetrical fault and the connection of the transformer windings. In this paper, the double line to ground fault is adopted for the voltage sag characterization. The depiction of the sage propagation is an outcome of the zero sequence components. Voltage sag propagation detection can be used to improve the power quality of the system. The obtained results reveal the characteristics of the voltage drop propagation from the primary to the secondary winding of the transformer throughout different connection. The implemented method will help analyze the Power Quality (PQ) problem in terms of voltage sag of the distributed transmission networks of the power system. [ABSTRACT FROM AUTHOR]

The poor Load Factor (LF) causes the tripping of transformers, heating of conducting material, failure of insulation and increase of the reactive power. Improving LF is an important issue to stabilize the power system. To investigate the power loss caused by the poor LF, the calculated peak and minimum load data loss at variable loads for times is analyzed. The low LF is calculated through collected data of minimum and maximum loads. The analysis of 7 feeders at 132/11kV grid station is carried out for this research purpose. The distribution system and analysis of load on different feeders are estimated through an investigation of site techniques. The major findings are discussed in the result and discussion section of this study. [ABSTRACT FROM AUTHOR]