Planning of Unbalanced Radial Distribution Systems with Reactive and Distributed Energy Sources Using Evolutionary Computing Techniques

The distribution system plays a key role in power system as it provides energy to the consumers safely, reliably, and economically. However, due to high R/X ratio, and low operating voltages, most of the losses occur in the distribution system. Moreover, distribution systems are generally unbalanced due to unequal single phase loads at the three phases of the system, and also additional unbalancing is introduced due to non-equilateral conductor spacing. This, causes the voltage, the current, and the power unbalance in the system. Further, the total neutral current of the system increases causing unwanted tripping of the relay. Hence, the service quality and the reliability of the distribution system reduces. Therefore, a suitable phase balancing strategy is required to mitigate the phase unbalancing in the unbalanced distribution systems. Also, apart from reducing the phase unbalancing in the unbalanced distribution systems, a suitable strategy is required to minimize the system power loss. In this regard, it is necessary for the distribution engineers to plan the unbalanced distribution systems in order to reduce the losses, voltage unbalances, and neutral current of the system for safe and reliable operation. Most of the approaches for the planning of the unbalanced distribution systems are based upon metaheuristic algorithms. Moreover, the recent research has focused only on either phase balancing or simultaneous phase balancing and conductor sizing optimization in unbalanced distribution systems using metaheuristic algorithms. However no work has been carried out to study the impact of the simultaneous optimization of the phase balancing, the conductor sizing, the capacitor location and sizing, the DG location and sizing, DSTATCOM location, and rating on system power loss, voltage unbalance, etc. utilizing these algorithms. As the metaheuristic algorithms are random in nature, the convergence is not guaranteed in a single simulation run. Hence, it is necessary to perform a statistical comparison among them in order to understand their relative merits and demerits for multiple simulation runs. In this thesis, the impact of the simultaneous optimization of the phase balancing and the conductor sizing on the planning problems/objective functions of the unbalanced distribution system such as; the power loss, the voltage unbalance, the total neutral current, and the complex power unbalance studied using various metaheuristic algorithms such as the DE, the CSA, the PSO, and the GA. In the first step, these objective functions are optimized separately; then they are aggregated with weights into a multi-objective optimization problem. Further, a performance comparison in terms of the mean value of the objective functions and standard deviation (SD) carried out. The reactive power compensating devices, such as the Capacitor, and the DSTATCOM has been integrated into the planning problem for the power loss minimization, the voltage profile improvement, and the voltage unbalance mitigation of the unbalanced distribution systems. Moreover, a three phase unbalanced modelling of the DSTATCOM has been developed. In this thesis, the effect of the simultaneous optimization of the phase balancing, the conductor sizing, the capacitor sizing, and the simultaneous optimization of the phase balancing, the conductor sizing, and the DSTATCOM sizing on the planning problem investigated. Both, single and multi-objective optimization approach are used in order to solve this problem. Also, statistical performance among the metaheuristic algorithms such as; the DE, the CSA, the PSO, and the GA in terms of the mean value of the objective function and SD carried out. Further, the renewable sources such as the DG and a combined DG and DSTATCOM has been incorporated into the unbalanced system in order to study their impact on various planning problems

Characterization of Properties and Estimation of Power Generation Potentials of Residues of Some Woody Biomass Species

In view of continuous increase in energy demand, and the environmental and economic concerns associated with the use of conventional fuels have made scientists and technocrats to look for alternative renewable energy sources for power production. The inherent advantages of carbon neutrality, lower ash content, lower SOx and NOx emissions, and wide availability have made biomass as a prime source of power generation. In this article, three different components taken from residues of five different woody plant species have been considered which have no commercial use. These plant species are Ficus benghalensis (local name- Banyan), Azadirachta indica (local name- Neem), Ficus religiosa (local name- Pippal), Madhuca longifolia (local name- Mahua) and Eucalyptus globulus (local name- Eucalyptus). Proximate analyses and gross calorific values (GCV) of all the biomass species including a coal sample have been determined. Among all the five biomass species studied, the fixed carbon content (FC) in Neem bark was observed to be the highest while its leaf has the lowest value, the volatile matter content (VM) in both Mahua branch and Eucalyptus leaf is the highest while Pippal bark has the lowest and the ash content (A) in bark of Mahua is the highest while the leaf of Eucalyptus biomass species has the lowest ash content. Similarly, the leaf of Eucalyptus is the most suitable one with the highest calorific value followed by leaves of Pippal and Mahua. Next in the order, the barks of Banyan and Neem, and the branches of Pippal, Mahua and Eucalyptus were also found to have considerably high amount of energy contents suitable for power generation. In addition, bulk densities of all the biomass species including the coal sample have been determined. Leaves of all the biomass species have been found to have lower bulk densities as compared to their barks and branches. It is worthy to note that among all the studied biomass species, branch of Eucalyptus has the highest bulk density while leaf of Neem has the lowest. Further, the ash fusion temperatures of some selected components of Banyan, Neem, Pippal and Mahua Page | XII biomass have been measured as these temperatures are the influential factors for the determination of bed agglomeration and other boiler fouling related problems. The results showed comparatively higher values of softening temperature ST (1077-1329 0C) and hemispherical temperature HT (1193-1450 0C) indicating safe boiler operation. Leaf and branch of Pippal and leaf and bark of Mahua were separately mixed with coal sample in different ratios, and their various percentage compositions related to proximate analyses and energy values were determined to explore the best coal-biomass mixture for power generation. It is evident from the results that the ash content decreased and volatile matter increased when the biomass percentage increased in the coal-biomass blend. The ultimate analysis has also been carried out on selected biomass species of Banyan, Mahua and Pippal. Carbon and Hydrogen contents of both Pippal and Mahua leaf were found to be higher and their corresponding calorific values were also high. The variation in energy values of plant components is undoubtedly related to the combined effects of their C and H contents. As the calorific value is the most salient property of any fuel, including biomass fuel, an attempt has been made to derive numerous regression equations using proximate and ultimate analysis data for prediction of gross calorific values of studied biomass species. The equations have been obtained statistically using regression analysis. The two linear regression equations with the best results obtained on the basis of proximate and ultimate analyses are GCV = – 49.02 + 0.968×FC + 0.719×VM + 0.459×A and GCV = 9.8 + 0.0613×O – 1.44×N – 0.829×C + 8.18×H respectively. The two nonlinear regression equations with best results obtained are GCV = 237.85 – 8.278×M – 5.723×VM – 3.098×FC – 0.055×M2 + 0.129M×VM + 0.089×M×FC + 0.0319×VM2 + 0.061×VM×FC – 0.021×FC2 and GCV = 70.408 + 0.153×O – 3.115×C + 1.035×H – 0.041×O2 + 0.101×O×C – 0.069×O×H – 0.0317×C2 + 1.217×H2 respectively. The results regarding computation of land requirement show that around 84, 618, 254, 148 and 289 hectares of land area are needed for energy plantation considering Page | XIII Banyan, Neem, Pippal, Mahua and Eucalyptus biomass species respectively. The above calculation serves the purpose of electricity generation of 7300 MWh per year for a cluster of 10-15 villages on decentralized power generation mode. Further, the requirements of blends of coal-Pippal branch and coal-Mahua bark to generate 7300 MWh/year of electricity was calculated and it was observed that the requirement of coal decreases with increase in the percentage of biomass in these blends. In case of coal-Pippal branch blend, the requirement of coal decreased from 5798 t/year to 5038 t/year and in coal-Mahua bark blend, coal requirement reduced from 5798 t/year to 5076 t/year as both biomass contents increased from 0 to 15%

An Improved ANN Approach for Occupancy Detection of A Smart Building

Building energy performance can be improved with a reduction in energy consumption. The heating and cooling loads of a building are important factors to consider in the field of energy conservation. It is possible to estimate energy consumption by predicting the presence of occupants in a room based on information provided by the HVAC (Heating, Ventilation, and Air Conditioning) system using standard information. Temperature, humidity, light, and CO2 levels from various sensors are taken as input parameters. In addition, the output of the network is programmed to be "0" when the building is not occupied and "1" when the building is occupied for the purpose of occupant detection. Pattern recognition using an Elman back propagation network is being proposed for occupancy detection. The data sets were used for training and testing (with the office door open and closed) the models during occupancy. The proposed ANN-based method is trained and tested and was found to be more effective, with an accuracy of 98.5% and 97.5% in cases of closed and opened doors, respectively

Tyrannosaurus optimization algorithm: A new nature-inspired meta-heuristic algorithm for solving optimal control problems

Recently, the optimal control problem has gained much importance for solving practical problems. In this regard, the meta-heuristic algorithms are proven to be effective while solving these problems effectively and efficiently. However, these algorithms may not be effective for solving all the optimization problems as per the no free lunch theorem. Thus, there is always a scope of development of new meta-heuristic algorithms. This paper proposes a new hunting-based optimization algorithm called Tyrannosaurus (T-Rex) optimization algorithm (TROA). This algorithm is inspired by the hunting behavior of the T-Rex. This algorithm was tested on 12 benchmark problems and 4 practical optimal control problems. The performance of the TROA is compared with seven famous optimization techniques, i.e. Differential Evolution (DE) Algorithm, Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), White Shark Optimizer (WSO), Jellyfish Search (JS), Crow Search Algorithm (CSA), Golden Eagle Optimization (GEO). The results obtained for the proposed method have given better when compared to these methods