Phasor measurement unit (PMU) technology is a need of the power system due to its better resolution than conventional estimation devices used for wide-area monitoring. PMUs can provide synchronized phasor and magnitude of voltage and current measurements for state estimation of the power system to prevent blackouts. The drawbacks of a PMU are the high cost of the device and its installation. The main aspect of using PMUs in electrical networks is the property to observe the adjacent buses, thereby making it possible to observe the system with fewer PMUs than the number of buses through their optimal placement. In the last two decades, exhaustive research has been done on this issue. Considering the importance of this field, a comprehensive review of the progress achieved until now is carried out and the limitations of existing reviews in the literature are highlighted. This paper can be seen as a major attempt to provide an up-to-date review of the research work carried out in this all-important field of PMU placement and indicates that some perspectives of optimal PMU placement still need attention. Eventually, the work will open a new standpoint for the research community to fill the research gap.

In this paper, a coordination method of multiple electric vehicle (EV) aggregators has been devised to flatten the system load profile. The proposed scheme tends to reduce the peak demand by discharging EVs and fills the valley gap through EV charging in the off-peak period. Upper level fair proportional power distribution to the EV aggregators is exercised by the system operator which provides coordination among the aggregators based on their aggregated energy demand or capacity. The lower level min max objective function is implemented at each aggregator to distribute power to the EVs. Each aggregator ensures that the EV customers’ driving requirements are not relinquished in spite of their employment to support the grid. The scheme has been tested on IEEE 13-node distribution system and an actual distribution system situated in Seoul, Republic of Korea whilst utilizing actual EV mobility data. The results show that the system load profile is smoothed by the coordination of aggregators under peak shaving and valley filling goals. Also, the EVs are fully charged before departure while maintaining a minimum energy for emergency travel.

Induction motors (IMs) are the backbone of industry, and play a vital role in daily life as well. However, induction motors face various faults during their operation, which may cause overheating, energy losses, and failure in the motors. Keeping in mind the severity of the issues associated with fault occurrence, this paper proposes a novel method of fault detection in induction motors by using “Machine Vision (MV)” along with “Infrared Thermography (IRT)”. It is worth mentioning that the timely prevention of faults in the IM ensures the motor’s safety from failures, and provides longer service life. In this work, a dataset of thermal images of an induction motor under different conditions (i.e., normal operation, overloaded, and fault) was developed using an infrared camera without disturbing the working condition of the motor. Then, the extracted thermal images were effectively used for the feature extraction and training by local octa pattern (LOP) and support-vector machine (SVM) classifiers, respectively. In order to enhance the quality of feature extraction from images, the LOP was implemented along with a genetic algorithm (GA). Finally, the proposed methodology was implemented and validated by detecting the faults introduced in an induction motor in real time. In addition to that, a comparative study of the suggested methodology with existing methods also verified the supremacy and effectiveness of the proposed method in comparison to the previous techniques.

Due to an increase in penetration of intermittent distributed energy resources (DERs) in conjunction with load demand escalation, the electric power system will confront more and more challenges in terms of stability and reliability. Furthermore, the adoption of electric vehicles (EVs) is increasing day by day in the personal automobile market. The sudden rise in load demand due to EV load might cause overloading of the potential transformer, undue circuit faults and feeder congestion. The objective of this paper is to develop a strategy for distribution feeder management to support the implementation of emergency demand response (EDR) during contingency and overload conditions. The proposed methodology focuses on management of smart home appliances along with EVs by considering demand rebound and consumer convenience indices, in order to reduce network stress, congestion and demand rebound. The developed scheme ensures that the load profile is retained below a certain level during a demand response event while mitigating demand rebound impacts. Simultaneously, the mitigation of consumers’ convenience level violation, information of smart loads and homeowners’ objective of serving critical loads are also considered during the event. The effectiveness of the developed approach is assessed by simulating a node of a distribution network of 300kW, consisting of 9 distribution transformers serving the associated homes. In this study, the smart loads such as an air conditioner/heater, an EV, a clothes dryer, and a water heater are also modeled and simulated. Furthermore, the simulation results are compared with an already developed de-centralized approach, and a simple fair distribution approach to evaluate and validate the effectiveness of the designed methodology. It is exhibited by the analysis of the results that the developed approach reduced the demand rebound following a demand response event and minimized the congestion at distribution transformer during overloading condition while maintaining the consumers’ comfort.

With the recent developments in power electronics technologies, increased deployment of distributed energy resources (DER) with DC output type at distribution voltage levels and significant increase in the number of sensitive AC and DC loads integrated in distribution network have enforced the traditional power network in the continuous renovation process. In this paper, the load flow solution of hybrid AC/DC distribution networks with the multi-terminal configuration is studied. The impact of voltage source converter (VSC) losses and AC and DC line losses in the presence of DER in the distribution system are assessed. The motivation of this analysis is to consider an increase in the number of converter stations which might result in non-negligible converter losses and the presence of various DER within the network imposing different network scenarios. The proposed schemes are simulated on two modified IEEE 33 bus hybrid AC/DC distribution network test system equipped with VSC-MTDC and the results are presented. Obtained results show that by considering the network losses and the converter losses with large number of converters within the network could lead to very different load flow solution and power transfer between networks, especially considering the AC or DC bus dominated network.

The optimal PMU placement problem is placing the minimum number of PMUs in the network to ensure complete network observability. It is an NP-complete optimization problem. PMU placement based on cost and critical nodes is solved separately in the literature. This paper proposes a novel approach, a degree of centrality in the objective function, to combine the effect of both strategies to place PMUs in the power network optimally. The contingency analysis and the effect of zero-injection buses are solved to ensure the reliability of network monitoring and attain a minimum number of PMUs. Integer linear programming is used on the IEEE 7-bus, IEEE 14-bus, IEEE 30-bus, New England 39-bus, IEEE 57-bus, and IEEE 118-bus systems to solve this problem. The results are evaluated based on two performance measures: the bus observability index (BOI) and the sum of redundancy index (SORI). On comparison, it is found that the proposed methodology has significantly improved results, i.e., a reduced number of PMUs and increased network overall observability (SORI). This methodology is more practical for implementation as it focuses on critical nodes. Along with improvement in the results, the limitations of existing indices are also discussed for future work.