Smart Distributed Generation System Event Classification using Recurrent Neural Network-based Long Short-term Memory

High penetration of distributed generation (DG) sources into a decentralized power system causes several disturbances, making the monitoring and operation control of the system complicated. Moreover, because of being passive, modern DG systems are unable to detect and inform about these disturbances related to power quality in an intelligent approach. This paper proposed an intelligent and novel technique, capable of making real-time decisions on the occurrence of different DG events such as islanding, capacitor switching, unsymmetrical faults, load switching, and loss of parallel feeder and distinguishing these events from the normal mode of operation. This event classification technique was designed to diagnose the distinctive pattern of the time-domain signal representing a measured electrical parameter, like the voltage, at DG point of common coupling (PCC) during such events. Then different power system events were classified into their root causes using long short-term memory (LSTM), which is a deep learning algorithm for time sequence to label classification. A total of 1100 events showcasing islanding, faults, and other DG events were generated based on the model of a smart distributed generation system using a MATLAB/Simulink environment. Classifier performance was calculated using 5-fold cross-validation. The genetic algorithm (GA) was used to determine the optimum value of classification hyper-parameters and the best combination of features. The simulation results indicated that the events were classified with high precision and specificity with ten cycles of occurrences while achieving a 99.17% validation accuracy. The performance of the proposed classification technique does not degrade with the presence of noise in test data, multiple DG sources in the model, and inclusion of motor starting event in training samples

Performance and cost benefit analyses of university campus microgrid.

Doctoral Degree. University of KwaZulu- Natal, Durban.Affordable and clean energy is one of the sustainable development goals (SDGs) to be achieved by the year 2030. Renewable energy sources such as wind, hydro, solar are free and inexhaustible globally to produce clean, reliable and cost effective power. However, most renewable energy sources are intermittent, to overcome this barrier, the concept of microgrid has been deployed in many applications to aggregate renewable energy resources, energy storage system and energy management system for sustainable, reliable, economical and environmental - friendly power system. Furthermore, considering the continuous increase in the cost of electricity and recent load shedding in South Africa, universities can reduce cost of energy demand, avoid interruption of academic activities due to load shedding and develop a test-bed or laboratory in which students and faculty staff can conduct research to advance modern power system through a self-sustaining microgrid. The university is like a separate entity and can operate as an island with sufficient resources to meet her energy demands. This thesis analyses the performance of a university campus microgrid using the five campuses of the University of Kwa-Zulu Natal as case studies considering economical and environmental benefits. Three different studies are carried out to achieve the aim and objectives of this work. The first study considers a grid connected microgrid using the real time data from the university energy management system, the modelling and simulations are implemented in HOMER Grid®. The main objective is to determine the optimal generation mix and size of a hybrid system consisting of the utility (eThekwini Electricity), solar PV, wind turbine, diesel generator and battery system taking into consideration the cost of energy (COE), net present cost (NPC), return on investment (ROI), payback period (PBP), utility cost saving and CO2 emission reduction. The second study aims to optimize the operational cost of a hybrid power system (PV-Wind-Diesel Generator-Battery) using two campuses as case studies. The objective function is formulated as a non-linear cost function and solved using a MATLAB function, ‘quadprog’ considering daily demands during summer and winter study and vacation periods with the aim of comparing the fuel costs and assess the effectiveness of the hybrid system. The third study proposes a novel optimization algorithm, the Quantum-behaved bat algorithm (QBA) to solve combined economic and emission dispatch (CEED) problem in an off-grid microgrid with onsite thermal generators and renewable energy sources (PV and Wind). The results obtained from these studies show and validate the fact that renewable energy source (RES) can be used to meet university energy demands in an economical way and reduce carbon footprint on campuses. It is observed from the result that the annual utility bill savings range from R3.97 million to R17.42 million and directly proportional to the peak load. The average emission reduction for all campuses is 49.6% except Pietermaritzburg where it is 33.7 %. In addition, the results will help university management as well as city management to invest wisely in renewables for energy sustainability and reliability