A neural network based automatic generation control design through reinforcement learning

This paper presents the design and implementation of a learning controller for the Automatic Generation Control (AGC) in power systems based on a reinforcement learning (RL) framework. In contrast to the recent RL scheme for AGC proposed by us, the present method permits handling of power system variables such as Area Control Error (ACE) and deviations from scheduled frequency and tie-line flows as continuous variables. (In the earlier scheme, these variables have to be quantized into finitely many levels). The optimal control law is arrived at in the RL framework by making use of Q-learning strategy. Since the state variables are continuous, we propose the use of Radial Basis Function (RBF) neural networks to compute the Q-values for a given input state. Since, in this application we cannot provide training data appropriate for the standard supervised learning framework, a reinforcement learning algorithm is employed to train the RBF network. We also employ a novel exploration strategy, based on a Learning Automata algorithm,for generating training samples during Q-learning. The proposed scheme, in addition to being simple to implement, inherits all the attractive features of an RL scheme such as model independent design, flexibility in control objective specification, robustness etc. Two implementations of the proposed approach are presented. Through simulation studies the attractiveness of this approach is demonstrated.

Neural network based power system damping controller for SVC

The development of a neural network based power system damping controller (PSDC) for a static VAr compensator (SVC), designed to enhance the damping characteristics of a power system network representing a part of the Electricity Generating Authority of Thailand (EGAT) system is presented. The proposed stabilising controller scheme of the SVC consists of a neuro-identifier and a neuro-controller which have been developed based on a functional link network (FLN) model. A recursive online training algorithm has been utilised to train the two networks. The simulation results have been obtained under various operating conditions and disturbance cases to show that the proposed stabilising controller can provide a better damping to the low frequency oscillations, as compared to the conventional controllers. The effectiveness of the proposed stabilising controller has also been compared with a conventional power system stabiliser provided in the generator excitation system

Neural network based power system damping controller for SVC

The development of a neural network based power system damping controller (PSDC) for a static Var compensator (SVC), designed to enhance the damping characteristics of a power system network representing a part of the Electricity Generating Authority of Thailand (EGAT) system is presented. The proposed stabilising controller scheme of the SVC consists of a neuro-identifier and a neuro-controller which have been developed based on a functional link network (FLN) model. A recursive online training algorithm has been utilised to train the two networks. The simulation results have been obtained under various operating conditions and disturbance cases to show that the proposed stabilising controller can provide a better damping to the low frequency oscillations, as compared to the conventional controllers. The effectiveness of the proposed stabilising controller has also been compared with a conventional power system stabiliser provided in the generator excitation system.

A neural network based power system stabilizer suitable for on-line training-a practical case study for EGAT system

This paper presents the development of a neural network based power system stabilizer (PSS) designed to enhance the damping characteristics of a practical power system network representing a part of Electricity Generating Authority of Thailand (EGAT) system. The proposed PSS consists of a neuro-identifier and a neuro-controller which have been developed based on functional link network (FLN) model. A recursive on-line training algorithm has been utilized to train the two neural networks. Simulation results have been obtained under various operating conditions and severe disturbance cases which show that the proposed neuro-PSS can provide a better damping to the local as well as interarea modes of oscillations as compared to a conventional PSS

Artificial neural network application to power system voltage stability improvement

As power systems grow in their size and interconnections, their complexity increases. Rising costs due to inflation and increased environmental concerns has made transmission, as well as generation systems be operated closer to design limits. Hence power system voltage stability and voltage control are emerging as major problems in the day-to-day operation of stressed power systems. For secure operation and control of power systems under normal and contingency conditions it is essential to provide solutions in real time to the operator in energy control center (ECC). Artificial neural networks (ANN) are emerging as an artificial intelligence tool, which give fast, though approximate, but acceptable solutions in real time as they mostly use the parallel processing technique for computation. The solutions thus obtained can be used as a guide by the operator in ECC for power system control. This paper deals with development of an ANN architecture, which provide solutions for monitoring, and control of voltage stability in the day-to-day operation of power systems.

Novel algorithm for online voltage stability assessment based on feed forward neural network

This paper presents an artificial feed forward neural network (FFNN) approach for the assessment of power system voltage stability. A novel approach based on the input-output relation between real and reactive power, as well as voltage vectors for generators and load buses is used to train the neural net (NN). The input properties of the feed forward network are generated from offline training data with various simulated loading conditions using a conventional voltage stability algorithm based on the L-index. The neural network is trained for the L-index output as the target vector for each of the system loads. Two separate trained NN, corresponding to normal loading and contingency, are investigated on the 367 node practical power system network. The performance of the trained artificial neural network (ANN) is also investigated on the system under various voltage stability assessment conditions. As compared to the computationally intensive benchmark conventional software, near accurate results in the value of L-index and thus the voltage profile were obtained. Proposed algorithm is fast, robust and accurate and can be used online for predicting the L-indices of all the power system buses. The proposed ANN approach is also shown to be effective and computationally feasible in voltage stability assessment as well as potential enhancements within an overall energy management system in order to determining local and global stability indices

Prediction of Ground Water Levels in the Uplands of a Tropical Coastal Riparian Wetland using Artificial Neural Networks

Artificial Neural Networks (ANNs) have been found to be a robust tool to model many non-linear hydrological processes. The present study aims at evaluating the performance of ANN in simulating and predicting ground water levels in the uplands of a tropical coastal riparian wetland. The study involves comparison of two network architectures, Feed Forward Neural Network (FFNN) and Recurrent Neural Network (RNN) trained under five algorithms namely Levenberg Marquardt algorithm, Resilient Back propagation algorithm, BFGS Quasi Newton algorithm, Scaled Conjugate Gradient algorithm, and Fletcher Reeves Conjugate Gradient algorithm by simulating the water levels in a well in the study area. The study is analyzed in two cases-one with four inputs to the networks and two with eight inputs to the networks. The two networks-five algorithms in both the cases are compared to determine the best performing combination that could simulate and predict the process satisfactorily. Ad Hoc (Trial and Error) method is followed in optimizing network structure in all cases. On the whole, it is noticed from the results that the Artificial Neural Networks have simulated and predicted the water levels in the well with fair accuracy. This is evident from low values of Normalized Root Mean Square Error and Relative Root Mean Square Error and high values of Nash-Sutcliffe Efficiency Index and Correlation Coefficient (which are taken as the performance measures to calibrate the networks) calculated after the analysis. On comparison of ground water levels predicted with those at the observation well, FFNN trained with Fletcher Reeves Conjugate Gradient algorithm taken four inputs has outperformed all other combinations.

Application of Artificial Neural Networks and Particle Swarm Optimization for the Management of Groundwater Resources

Ground management problems are typically solved by the simulation-optimization approach where complex numerical models are used to simulate the groundwater flow and/or contamination transport. These numerical models take a lot of time to solve the management problems and hence become computationally expensive. In this study, Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) models were developed and coupled for the management of groundwater of Dore river basin in France. The Analytic Element Method (AEM) based flow model was developed and used to generate the dataset for the training and testing of the ANN model. This developed ANN-PSO model was applied to minimize the pumping cost of the wells, including cost of the pipe line. The discharge and location of the pumping wells were taken as the decision variable and the ANN-PSO model was applied to find out the optimal location of the wells. The results of the ANN-PSO model are found similar to the results obtained by AEM-PSO model. The results show that the ANN model can reduce the computational burden significantly as it is able to analyze different scenarios, and the ANN-PSO model is capable of identifying the optimal location of wells efficiently.

A low-noise low-power noise-adaptive neural amplifier in 0.13um CMOS technology

Chronic recording of neural signals is indispensable in designing efficient brain machine interfaces and in elucidating human neurophysiology. The advent of multichannel microelectrode arrays has driven the need for electronics to record neural signals from many neurons. The dynamic range of the system is limited by background system noise which varies over time. We propose a neural amplifier in UMC 130 nm, 2P8M CMOS technology. It can be biased adaptively from 200 nA to 2 uA, modulating input referred noise from 9.92 uV to 3.9 uV. We also describe a low noise design technique which minimizes the noise contribution of the load circuitry. The amplifier can pass signal from 5 Hz to 7 kHz while rejecting input DC offsets at electrode-electrolyte interface. The bandwidth of the amplifier can be tuned by the pseudo-resistor for selectively recording low field potentials (LFP) or extra cellular action potentials (EAP). The amplifier achieves a mid-band voltage gain of 37 dB and minimizes the attenuation of the signal from neuron to the gate of the input transistor. It is used in fully differential configuration to reject noise of bias circuitry and to achieve high PSRR.

IGF-1 stimulated upregulation of cyclin D1 is mediated via STAT5 signaling pathway in neuronal cells

Signal Transducer and Activator of Transcription (STATs) regulate various target genes such as cyclin D1, MYC, and BCL2 in nonneuronal cells which contribute towards progression as well as prevention of apoptosis and are involved in differentiation and cell survival. However, in neuronal cells, the role of STATs in the activation and regulation of these target genes and their signaling pathways are still not well established. In this study, a robust cyclin D1 expression was observed following IGF-1 stimulation in SY5Y cells as well as neurospheres. JAK/STAT pathway was shown to be involved in this upregulation. A detailed promoter analysis revealed that the specific STAT involved was STAT5, which acted as a positive regulatory element for cyclin D1 expression. Overexpression studies confirmed increase in cyclin D1 expression in response to STAT5a and STAT5b constructs when compared to dominant-negative STAT5. siRNA targeting STAT5, diminished the cyclin D1 expression, further confirming that STAT5 specifically regulated cyclin D1 in neuronal cells. Together, these findings shed new light on the mechanism of IGF-1 mediated upregulation of cyclin D1 expression in neural cell lines as well as in neural stem cells via the JAK/STAT5 signaling cascade.

Multispecies coexistence of trees in tropical forests: spatial signals of topographic niche differentiation increase with environmental heterogeneity

Neutral and niche theories give contrasting explanations for the maintenance of tropical tree species diversity. Both have some empirical support, but methods to disentangle their effects have not yet been developed. We applied a statistical measure of spatial structure to data from 14 large tropical forest plots to test a prediction of niche theory that is incompatible with neutral theory: that species in heterogeneous environments should separate out in space according to their niche preferences. We chose plots across a range of topographic heterogeneity, and tested whether pairwise spatial associations among species were more variable in more heterogeneous sites. We found strong support for this prediction, based on a strong positive relationship between variance in the spatial structure of species pairs and topographic heterogeneity across sites. We interpret this pattern as evidence of pervasive niche differentiation, which increases in importance with increasing environmental heterogeneity.

Substrate conductivity dependent modulation of cell proliferation and differentiation in vitro

In designing and developing various biomaterials, the influence of substrate properties, like surface topography, stiffness and wettability on the cell functionality has been investigated widely. However, such study to probe into the influence of the substrate conductivity on cell fate processes is rather limited. In order to address this issue, spark plasma sintered HA-CaTiO3 (Hydroxyapatite-Calcium titanate) has been used as a model material system to showcase the effect of varying conductivity on cell functionality. Being electroactive in nature, mouse myoblast cells (C2C12) were selected as a model cell line in this study. It was inferred that myoblast adhesion/growth systematically increases with substrate conductivity due to CaTiO3 addition to HA. Importantly, parallel arrangement of myoblast cells on higher CaTiO3 containing substrates indicate that self-adjustable cell patterning can be achieved on conductive biomaterials. Furthermore, enhanced myoblast assembly and myotube formation were recorded after 5 days of serum starvation. Overall, the present study conclusively establishes the positive impact of the substrate conductivity towards cell proliferation and differentiation as well as confirms the efficacy of HA-CaTiO3 biocomposites as conductive platforms to facilitate the growth, orientation and fusion of myoblasts, even when cultured in the absence of external electric field.

Nanolipodendrosome-loaded glatiramer acetate and myogenic differentiation I as augmentation therapeutic strategy approaches in muscular dystrophy

Backgrond: Muscular dystrophies consist of a number of juvenile and adult forms of complex disorders which generally cause weakness or efficiency defects affecting skeletal muscles or, in some kinds, other types of tissues in all parts of the body are vastly affected. In previous studies, it was observed that along with muscular dystrophy, immune inflammation was caused by inflammatory cells invasion - like T lymphocyte markers (CD8+/CD4+). Inflammatory processes play a major part in muscular fibrosis in muscular dystrophy patients. Additionally, a significant decrease in amounts of two myogenic recovery factors (myogenic differentation 1 MyoD] and myogenin) in animal models was observed. The drug glatiramer acetate causes anti-inflammatory cytokines to increase and T helper (Th) cells to induce, in an as yet unknown mechanism. MyoD recovery activity in muscular cells justifies using it alongside this drug. Methods: In this study, a nanolipodendrosome carrier as a drug delivery system was designed. The purpose of the system was to maximize the delivery and efficiency of the two drug factors, MyoD and myogenin, and introduce them as novel therapeutic agents in muscular dystrophy phenotypic mice. The generation of new muscular cells was analyzed in SW1 mice. Then, immune system changes and probable side effects after injecting the nanodrug formulations were investigated. Results: The loaded lipodendrimer nanocarrier with the candidate drug, in comparison with the nandrolone control drug, caused a significant increase in muscular mass, a reduction in CD4+/CD8+ inflammation markers, and no significant toxicity was observed. The results support the hypothesis that the nanolipodendrimer containing the two candidate drugs will probably be an efficient means to ameliorate muscular degeneration, and warrants further investigation.

Aza-induced cardiomyocyte differentiation of P19 EC-cells by epigenetic co-regulation and ERK signaling

Stem cells in cell based therapy for cardiac injury is being potentially considered. However, genetic regulatory networks involved in cardiac differentiation are not clearly understood. Among stem cell differentiation models, mouse P19 embryonic carcinoma (EC) cells, are employed for studying (epi)genetic regulation of cardiomyocyte differentiation. Here, we comprehensively assessed cardiogenic differentiation potential of 5-azacytidine (Aza) on P19 EC-cells, associated gene expression profiles and the changes in DNA methylation, histone acetylation and activated-ERK signaling status during differentiation. Initial exposure of Aza to cultured EC-cells leads to an efficient (55%) differentiation to cardiomyocyte-rich embryoid bodies with a threefold (16.8%) increase in the cTnI(+) cardiomyocytes. Expression levels of cardiac-specific gene markers i.e., Isl-1, BMP-2, GATA-4, and alpha-MHC were up-regulated following Aza induction, accompanied by differential changes in their methylation status particularly that of BMP-2 and alpha-MHC. Additionally, increases in the levels of acetylated-H3 and pERK were observed during Aza-induced cardiac differentiation. These studies demonstrate that Aza is a potent cardiac inducer when treated during the initial phase of differentiation of mouse P19 EC-cells and its effect is brought about epigenetically and co-ordinatedly by hypo-methylation and histone acetylation-mediated hyper-expression of cardiogenesis-associated genes and involving activation of ERK signaling.