Evanescent Wave Fibre Optic Sensors for Trace Analysis of Fe3+ in Water

In this communication, we discuss the details of fabricating an off-line fibre optic sensor (FOS) based on evanescent wave absorption for detecting trace amounts of Fe3+ in water. Two types of FOS are developed; one type uses the unclad portion of a multimode silica fibre as the sensing region whereas the other employs the microbent portion of a multimode plastic fibre as the sensing region. Sensing is performed by measuring the absorption of the evanescent wave in a reagent medium surrounding the sensing region. To evaluate the relative merits of the two types of FOS in Fe3+ sensing, a comparative study of the sensors is made, which reveals the superiority of the latter in many respects, such as smaller sensing length, use of a double detection scheme (for detecting both core and cladding modes) and higher sensitivity of cladding mode detection at an intermediate range of concentration along with the added advantage that plastic fibres are inexpensive. A detection limit of 1 ppb is observed in both types of fibre and the range of detection can be as large as 1 ppb–50 ppm. All the measurements are carried out using a LabVIEW set-up.

Evanescent wave fibre optic sensors for trace analysis of Fe3+ in water

In this communication, we discuss the details of fabricating an off-line fibre optic sensor (FOS) based on evanescent wave absorption for detecting trace amounts of Fe3+ in water. Two types of FOS are developed; one type uses the unclad portion of a multimode silica fibre as the sensing region whereas the other employs the microbent portion of a multimode plastic fibre as the sensing region. Sensing is performed by measuring the absorption of the evanescent wave in a reagent medium surrounding the sensing region. To evaluate the relative merits of the two types of FOS in Fe3+ sensing, a comparative study of the sensors is made, which reveals the superiority of the latter in many respects, such as smaller sensing length, use of a double detection scheme (for detecting both core and cladding modes) and higher sensitivity of cladding mode detection at an intermediate range of concentration along with the added advantage that plastic fibres are inexpensive. A detection limit of 1 ppb is observed in both types of fibre and the range of detection can be as large as 1 ppb–50 ppm. All the measurements are carried out using a LabVIEW set-up.

Evanescent wave fibre optic sensors for trace analysis of Fe3+ in water

In this communication, we discuss the details of fabricating an off-line fibre optic sensor (FOS) based on evanescent wave absorption for detecting trace amounts of Fe3+ in water. Two types of FOS are developed; one type uses the unclad portion of a multimode silica fibre as the sensing region whereas the other employs the microbent portion of a multimode plastic fibre as the sensing region. Sensing is performed by measuring the absorption of the evanescent wave in a reagent medium surrounding the sensing region. To evaluate the relative merits of the two types of FOS in Fe3+ sensing, a comparative study of the sensors is made, which reveals the superiority of the latter in many respects, such as smaller sensing length, use of a double detection scheme (for detecting both core and cladding modes) and higher sensitivity of cladding mode detection at an intermediate range of concentration along with the added advantage that plastic fibres are inexpensive. A detection limit of 1 ppb is observed in both types of fibre and the range of detection can be as large as 1 ppb–50 ppm. All the measurements are carried out using a LabVIEW set-up.

Evanescent wave fibre optic sensors for trace analysis of Fe3+ in water

In this communication, we discuss the details of fabricating an off-line fibre optic sensor (FOS) based on evanescent wave absorption for detecting trace amounts of Fe3+ in water. Two types of FOS are developed; one type uses the unclad portion of a multimode silica fibre as the sensing region whereas the other employs the microbent portion of a multimode plastic fibre as the sensing region. Sensing is performed by measuring the absorption of the evanescent wave in a reagent medium surrounding the sensing region. To evaluate the relative merits of the two types of FOS in Fe3+ sensing, a comparative study of the sensors is made, which reveals the superiority of the latter in many respects, such as smaller sensing length, use of a double detection scheme (for detecting both core and cladding modes) and higher sensitivity of cladding mode detection at an intermediate range of concentration along with the added advantage that plastic fibres are inexpensive. A detection limit of 1 ppb is observed in both types of fibre and the range of detection can be as large as 1 ppb–50 ppm. All the measurements are carried out using a LabVIEW set-up.

A fibre optic evanescent wave sensor used for the detection of trace nitrites in water

A fibre optic technique for detecting trace amounts of nitrite compounds in water is described. The off-line fibre optic sensor outlined here is based on evanescent field absorption in a test solution formed by the reaction of nitrite compounds in water with suitable chemical reagents. A short unclad portion of a plastic clad silica fibre acts as the sensing region. The experimental results clearly establish the usefulness of the present technique for detecting very low concentrations of the order of 1 ppb (parts per billion) of nitrite compounds with a large dynamic range of 1–1000 ppb. Such a high sensitivity enables the present device to be used for measuring the nitrite content in drinking water.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Short-Term Load Forecast Of A Low Loadfactor Power System For Optimization Of Merit Order Dispatch Using Adaptive Learning Algorithm

Short term load forecasting is one of the key inputs to optimize the management of power system. Almost 60-65% of revenue expenditure of a distribution company is against power purchase. Cost of power depends on source of power. Hence any optimization strategy involves optimization in scheduling power from various sources. As the scheduling involves many technical and commercial considerations and constraints, the efficiency in scheduling depends on the accuracy of load forecast. Load forecasting is a topic much visited in research world and a number of papers using different techniques are already presented. The accuracy of forecast for the purpose of merit order dispatch decisions depends on the extent of the permissible variation in generation limits. For a system with low load factor, the peak and the off peak trough are prominent and the forecast should be able to identify these points to more accuracy rather than minimizing the error in the energy content. In this paper an attempt is made to apply Artificial Neural Network (ANN) with supervised learning based approach to make short term load forecasting for a power system with comparatively low load factor. Such power systems are usual in tropical areas with concentrated rainy season for a considerable period of the year

Reinforcement Learning Solution for Unit Commitment Problem Considering Minimum Start Up and Shut Down Times

Unit Commitment Problem (UCP) in power system refers to the problem of determining the on/ off status of generating units that minimize the operating cost during a given time horizon. Since various system and generation constraints are to be satisfied while finding the optimum schedule, UCP turns to be a constrained optimization problem in power system scheduling. Numerical solutions developed are limited for small systems and heuristic methodologies find difficulty in handling stochastic cost functions associated with practical systems. This paper models Unit Commitment as a multi stage decision making task and an efficient Reinforcement Learning solution is formulated considering minimum up time /down time constraints. The correctness and efficiency of the developed solutions are verified for standard test systems

Reinforcement Learning solution for Unit Commitment Problem through pursuit method

Unit commitment is an optimization task in electric power generation control sector. It involves scheduling the ON/OFF status of the generating units to meet the load demand with minimum generation cost satisfying the different constraints existing in the system. Numerical solutions developed are limited for small systems and heuristic methodologies find difficulty in handling stochastic cost functions associated with practical systems. This paper models Unit Commitment as a multi stage decision task and Reinforcement Learning solution is formulated through one efficient exploration strategy: Pursuit method. The correctness and efficiency of the developed solutions are verified for standard test systems

Fishery and biology of deep-sea chondrichthyans off the southwest coast of india

Elasmobranchs comprising sharks, skates and rays have traditionally formed an important fishery along the Indian coast. Since 2000, Indian shark fishermen are shifting their fishing operations to deeper/oceanic waters by conducting multi-day fishing trips, which has resulted in considerable changes in the species composition of the landings vis- a-vis those reported during the 1980’s and 1990’s. A case study at Cochin Fisheries Harbour (CFH), southwest coast of India during 2008-09 indicated that besides the existing gillnet–cum- hooks & line and longline fishery for sharks, a targeted fishery at depths >300-1000 m for gulper sharks (Centrophorus spp.) has emerged. In 2008, the chondrichthyan landings (excluding batoids) were mainly constituted by offshore and deep-sea species such as Alopias superciliosus (24.2%), Carcharhinus limbatus (21.1%), Echinorhinus brucus (8.2%), Galeocerdo cuvier (5.4%), Centrophorus spp. (7.3%) and Neoharriotta pinnata (4.2%) while the contribution by the coastal species such as Sphyrna lewini (14.8%), Carcharhinus sorrah (1.4%) and other Carcharhinus spp. has reduced. Several deep-sea sharks previously not recorded in the landings at Cochin were also observed during 2008-09. It includes Hexanchus griseus, Deania profundorum, Zameus squamulosus and Pygmy false catshark (undescribed) which have been reported for the first time from Indian waters. Life history characteristics of the major fished species are discussed in relation to the fishery and its possible impacts on the resource