Solving Multi-Objective Voltage Stability Constrained Power Transfer Capability Problem using Evolutionary Computation

Competitive market forces and the ever-growing load demand are two of the key issues that cause power systems to operate closer to their system stability boundaries. Open access has since introduced competition and therefore promotes inter-regional electrical power trades. However, the economic flows of electrical energy between interconnected regions are usually constrained by system physical limits, e.g. transmission lines capacity and generation active/reactive power capability etc. As such, there is a limitation to the capacity of electrical power that regions can export or import. This maximum allowable electrical power transfer is normally referred to as Total Transfer Capability (TTC). It is critical to understand that TTC does not necessarily represent a safe and reliable amount of inter-regional power transfer as one or more operational limits are usually binding when quantifying TTC. Hence, it is of interest that power system stability issues are being considered during power transfer capability assessment in order to provide a more appropriate and secure power transfer level.The aim of this paper is to formulate an Optimal Power Flow (OPF) algorithm, which is capable of evaluating inter-area power transfer capability considering mathematically-complex voltage collapse margins. Through a multi-objective optimization setup, the proposed OPF-based approach can reveal the nonlinear relationships, i.e. the pareto-optimal front, between transfer capability and voltage stability margins. The feasibility of this approach has been intensively tested on a 3-machine 9-bus and the IEEE 118-bus systems

Sea level anomaly assessment of SARAL/AltiKa mission using high and low resolution data

Peninsular Malaysia is located at the focal point of Sunda Shelf, encompassed by the South China Sea to the East and by Andaman Sea at Indian Ocean in the west that causes various phenomena relevant to sea level along Malaysian coast. When the monsoons strike, the effect of wind and other factors will change the variability of Sea Level Anomaly (SLA) along coastal Malaysia. Traditionally, sea level change is observed using tide gauge installed along Malaysian coastal area. However, the data obtained is limited to the tide gauge station area, the sea level data for the deep sea cannot be obtained and there is no long-term record of observation. Therefore, satellite altimeter is used as a new alternative which enables sea level data to be obtained from space observation and to monitor SLA via SARAL/AltiKa which available since 2013, thus complementing the tide gauge. The aim of this study is to derive SLA parameter from high and low resolution of satellite altimetry data. This study involved the acquisition of SLA data by using RADS and PEACHI (AVISO) database system from satellite mission SARAL/AltiKa. Sequentially, SLA data has been analysed and evaluated based on tide gauge data provided by using UHSLC system. Comparison between the high resolution (PEACHI) and low resolution (RADS) data has been made to evaluate the density of altimetry data in term of distance to coast. As a result, high resolution (PEACHI) data are more accurate for coastal application with root mean square error (RMSE) of ±0.14 metre level. The analysis shows that the footprint of high resolution altimetry data is denser than the low resolution altimetry data. Data from distance to coast for PEACHI achieved a satisfactory standard deviation of residual, which is ranged between 0 cm to 1.04 cm as compared to altimetry RADS which is ranged 0.34 cm to 12.57 cm. The results can be used by various agencies in planning and developing Malaysian coastal areas as well as in assisting the development of community economies such as fishery and tourism activities

An optimal strategy to maximise voltage stability using memetic algorithms based on swarm trajectory movements

Many power systems in the world today are operating closer to their stability boundaries, and thus it is critical for independent system operators (ISOs) to ensure that systems have adequate stability margins during operation in case of unexpected losses of system components. Failure to do so may result in a catastrophic widespread blackout, ie. system voltage collapses. This paper presents a novel memetic algorithm (MA)-based strategy to effectively maximise system voltage stability margins, through the optimum control of automatic voltage regulator (AVR) of generators, on-load tap changer (OLTC) of transformers and the sizes of shunt capacitors (SCs) etc, given any system operating conditions. The proposed strategy can assist ISOs to perform corrective actions to increase stability margins when the system operates too close to the stability boundaries. A mix-integer non-linear programming (MINLP) problem is formulated here using a MA based on the trajectory movement rule of particle swarm optimisation (PSO). By using the MA-based approach, system voltage collapse margins can be improved and these enhancements can then be verified using a continuation power flow (CPF) technique. The feasibility and practicality of this approach has been tested on a 3-machine 9-bus and the IEEE 118-bus power systems

Do lipid-based nanoparticles hold promise for advancing the clinical translation of anticancer alkaloids?

SIMPLE SUMMARY: Alkaloids are natural products that possess numerous pharmacological activities and have been exploited effectively to treat cancer. However, the clinically approved anticancer alkaloids are generally limited by serious side effects due to their lack of specificity to cancer cells, indiscriminate tissue distribution and toxic formulation excipients. Lipid-based nanoparticles represent the most effective drug delivery system concerning clinical translation owing to their unique appealing characteristics for drug delivery. This review aims to assess the potential of different types of lipid-based nanoparticles in encapsulating anticancer alkaloids. Our review shows that alkaloids encapsulated in lipid-based nanoparticles generally displayed an enhanced efficacy and toxicity profile than unencapsulated alkaloids in various cancers. Encapsulated alkaloids also demonstrated the ability to overcome multidrug resistance in cell lines and animal models. These findings support the broad application of lipid-based nanoparticles to encapsulate anticancer alkaloids and facilitate their clinical translation. ABSTRACT: Since the commercialization of morphine in 1826, numerous alkaloids have been isolated and exploited effectively for the betterment of mankind, including cancer treatment. However, the commercialization of alkaloids as anticancer agents has generally been limited by serious side effects due to their lack of specificity to cancer cells, indiscriminate tissue distribution and toxic formulation excipients. Lipid-based nanoparticles represent the most effective drug delivery system concerning clinical translation owing to their unique, appealing characteristics for drug delivery. To the extent of our knowledge, this is the first review to compile in vitro and in vivo evidence of encapsulating anticancer alkaloids in lipid-based nanoparticles. Alkaloids encapsulated in lipid-based nanoparticles have generally displayed enhanced in vitro cytotoxicity and an improved in vivo efficacy and toxicity profile than free alkaloids in various cancers. Encapsulated alkaloids also demonstrated the ability to overcome multidrug resistance in vitro and in vivo. These findings support the broad application of lipid-based nanoparticles to encapsulate anticancer alkaloids and facilitate their clinical translation. The review then discusses several limitations of the studies analyzed, particularly the discrepancies in reporting the pharmacokinetics, biodistribution and toxicity data. Finally, we conclude with examples of clinically successful encapsulated alkaloids that have received regulatory approval and are undergoing clinical evaluation