A hybrid of ant colony optimization and artificial bee colony algorithm for probabilistic optimal placement and sizing of distributed energy resources

A novel integration technique for optimal network reconfiguration and distributed generation placement in power distribution networks

Multi-objective quasi-oppositional teaching learning based optimization for optimal location of distributed generator in radial distribution systems

Optimal placement of DG in radial distribution systems based on new voltage stability index under load growth

Distributed generation can be defined as power generation by small scale generating units that are installed at distribution systems. The penetration of distributed generation (DG) units in electric distribution systems is continually increasing. The process of finding optimal type, location and size of DG units is called “DG allocation”. DG allocation is a hot area of research and represents a difficult problem in electrical power engineering. In this paper, the existing research works on DG allocation problem are reviewed from viewpoint of their used optimisation algorithms, objectives, decision variables, DG type, applied constraints and kind of uncertainty modelling. Based on the review of existing research works, the research gaps are identified and some helpful recommendations for future research on DG allocation will be provided. The author strongly believes that this paper can be helpful for researchers and engineers in the related field.

Allocation of distributed generation units in electric power systems: A review

Enhancement of loading capacity of distribution system through distributed generator placement considering techno-economic benefits with load growth

Now a days power system protection is an important task for an operating engineer, which can be done by doing online security assessment. Contingency analysis is one of the best methods to forecast the condition of power system if any unwanted event occured in the power system. To do contingency analysis first the operator has to know the parameters like voltage, power and voltage angle at each and every bus by doing load flow analysis on the system. Newton Raphson method is the best load flow method as it gives accurate results in less time. In this paper all line outage contingencies in a standard 6 bus and 5 bus power system has been done in MATLAB environment. For each line outage contingency, load flow analysis has been done on the system and the active power and voltage performance indices have been calculated. These two performance indices will give the idea about the change in active power flow through the lines and voltages at the buses for a particular line outage. Summation of these two indices will give the performance index value through which ranking of severity will be given to the lines. And from the load flow results comparison has been done between low rank and high rank line outage contingencies. This contingency analysis helps the operational engineer to know which line outage is dangerous to the system and what prior action is to be taken to minimize the effect of that particular line outage.

Contingency analysis of power system by using voltage and active power performance index

Identification of faults within power transformers is the means of ensuring unit transformer protection. Existing relay maloperates during abnormalities such as magnetising inrush, CT saturation and high resistance internal fault condition. Therefore, it is essential to categorise the internal fault and external abnormality/fault in case of transformer protection. This study presents a new scheme, based on relevance vector machine (RVM) as a fault classifier. The developed algorithm is assessed by simulating various disorders on 345 MVA, 400/220 kV transformer in PSCAD/EMTDC software and also on prototype model with 2 kVA, 230/110 V multi-tapping transformer. One cycle post fault current signals are captured from primary and secondary to form feature vectors. These feature vectors are used as an input to RVM for classification of various test cases. Wide variation in system parameters and fault conditions are considered for test data generation and validation. The proposed scheme is compared with the support vector machine (SVM) and probabilistic neural network (PNN)-based techniques. The proposed scheme successfully discriminates various types of internal faults and external abnormalities in power transformer within a short time. The fault classification accuracy obtained by proposed RVM technique is more than 99% in comparison to SVM and PNN-based schemes.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-epa.2017.0562

Design and development of fault classification algorithm based on relevance vector machine for power transformer

Impact of demand response program in wind integrated distribution network

The penetration level of distributed generation (DG) has increasing day by day due to restructuring in electricity market. This paper discusses the sizing and sitting issue of single DG placement in radial distribution network. The main objective of this paper is to minimize active power loss and improve voltage profile of overall system. This paper presents three optimization techniques - Particle swarm optimization (PSO), Craziness based particle swarm optimization (CRPSO) and Gravitational Search algorithm (GSA) for optimal placement of distributed generators. The methods are tested with two IEEE standard test cases, 12-node radial distribution network and 69-node radial distribution network, and the results obtained by three algorithms are comparable.

Khyati D. Mistry

Papers

Enhancement of loading capacity of distribution system through distributed generator placement considering techno-economic benefits with load growth

Contingency analysis of power system by using voltage and active power performance index

Design and development of fault classification algorithm based on relevance vector machine for power transformer

Impact of demand response program in wind integrated distribution network

GSA based optimal capacity and location determination of distributed generation in radial distribution system for loss minimization