Decision making on transformer insulation condition based on the evaluated incipient faults and aging stresses has been the norm for many asset managers. Despite being the extensively applied methodology in power transformer incipient fault detection, solely dissolved gas analysis (DGA) techniques cannot quantify the detected fault severity. Fault severity is the core property in transformer maintenance rankings. This paper presents a fuzzy logic methodology in determining transformer faults and severity through use of energy of fault formation of the evolved gasses during transformer faulting event. Additionally, the energy of fault formation is a temperature-dependent factor for all the associated evolved gases. Instead of using the energy-weighted DGA, the calculated total energy of related incipient fault is used for severity determination. Severity of faults detected by fuzzy logic-based key gas method is evaluated through the use of collected data from several in-service and faulty transformers. DGA results of oil samples drawn from transformers of different specifications and age are used to validate the model. Model results show that correctly detecting fault type and its severity determination based on total energy released during faults can enhance decision-making in prioritizing maintenance of faulty transformers.

No abstract

No abstract

No abstract

The performance of voltage stability indices in the multiobjective optimal power flow of modern power systems is presented in this work. Six indices: the Voltage Collapse Proximity Index (VCPI), Line Voltage Stability Index (LVSI), Line Stability Index (Lmn), Fast Voltage Stability Index (FVSI), Line Stability Factor (LQP), and Novel Line Stability Index (NLSI) were considered as case studies on a modified IEEE 30-bus consisting of thermal, wind, solar and hybrid wind-hydro generators. A multiobjective evaluation using the multiobjective mayfly algorithm (MOMA) was performed in two operational scenarios: normal and contingency conditions, using the MATLAB–MATPOWER toolbox. Fuzzy Decision-Making technique was used to determine the best compromise solutions for each Pareto front. To evaluate the computational efficiency of the case studies, a preference selection index was used. The results indicate that VCPI and NLSI yielded the best-optimized system performance in minimizing generation costs, transmission loss reduction, and simulation time for normal and contingency conditions. The best-case studies also promoted the most scheduled reactive power generation from renewable energy sources (RES). On average, the VCPI index contributed the highest penetration level from RES (13.40%), while the Lmn index had the lowest. Overall, VCPI and Lmn index provided the best and worst average performance in both operating scenarios, respectively. Also, the MOMA algorithm demonstrated superior performance against the multiobjective harris hawks algorithm (MHHO), multiobjective Jaya algorithm (MOJAYA), multiobjective particle swarm algorithm (MOPSO), and nondominated sorting genetic algorithm III (NSGA-III) algorithms. In all, the proposed approach yields the lowest system cost and loss compared to other methods.

Voltage collapse tends to occur due to the voltage instability created during large faults. As a last resort, undervoltage load shedding (UVLS) is performed after all the available power operation and control mechanisms have been exhausted. Load shedding techniques have advanced from the conventional and adaptive methods that are less optimal compared to computational intelligence-based techniques. Recent works have identified hybrid algorithms to give more optimal solutions for UVLS problems with multi-objective functions. In this paper, a novel hybrid ABC-PSO algorithm, adapted from a software estimation project, is used to perform UVLS on a modified IEEE 14-bus system. Eight overload conditions are imposed on the system ranging from 105% to 140% loading, where FVSI ranking is used in identifying weak buses. The load shedding is then performed following decentralized relay settings of 3.5 seconds, 5 seconds and 8 seconds, which gives an overall 99.32% recovery of voltage profiles. The proposed hybrid ABC-PSO algorithm is able to shed optimal amounts of load, giving an 89.56% postcontingency load, compared to GA's 77.04%, ABC-ANN at 84.03% and PSO-ANN at 80.96%. This study has been simulated on MATLAB software, using the Power System Analysis Toolbox (PSAT) graphical user and commandline interfaces.

No abstract

The main aim of a power utility company is to supply quality and uninterrupted power to customers. This becomes a growing challenge as the continued increase in population calls for proportional increase in power supply to additional loads. If not well planned, this steady increase in power demand can lead to voltage collapse and eventual power blackouts. In instances where power demand exceeds generation within islanded microgrid or due to an occurrence of a contingency, optimum load shedding should be put in place so as to enhance system security and stability of the power system. Load shedding is traditionally done based on undervoltage measurements or underfrequency measurements of a given section of the grid. However, when compared with conventional methods, metaheuristic algorithms perform better in accurate determination of optimal amount of load to be shed during a contingency or undersupply situations. In this study, an islanded microgrid with high penetration of Renewable Energy Sources (RESs) is analyzed, and then Artificial Bee Colony (ABC) algorithm is applied for optimal load shedding. The results are then compared with those of Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and GA-PSO hybrid. Both generation and overload contingencies are considered on a standard IEEE 30-bus system on a MATLAB platform. Different buses are assigned priority indices which forms the basis of the determination of which loads and what amount of load to shed at any particular time.