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Ampere-hour (Ah) efficiency: The quantity of electricity measured in Ampere-hours which may be delivered by a cell or battery under specified conditions. Ampere-hour capacity: The total number of Ampere-hours or watt-hours that can be withdrawn from a fully charged cell, indicated by Ah or mAh. Battery: Two or more electrochemical cells connected together electrically in series, parallel, or both, to provide the required operating voltage and current levels. Crate: Charge or discharge current, in Ampere, expressed in multiples of the rated capacity. For example, C/10 charge current for a cell rated at 20 Ah is: 20 Ah/10 = 2 A. Capacity: See Ampere-hour capacity. Cell: The smallest electrochemical unit of a battery used to generate or store electrical energy. Coulombic efficiency: See Ampere-hour efficiency. Cutoff voltage: The cell voltage at which the discharge process is terminated (it is generally a function of discharge rate). Cycle life: The number of times a cell can be discharged and recharged until the cell capacity drops to a specified minimum value usually 80 % of rated capacity. Depth of discharge: The quantity of electricity (Ampere-hours) removed from a fully charged cell, expressed as a percentage of its rated Ampere-hour capacity. Energy density: The ratio of the energy available from a cell to its volume (Wh/L) or mass (Wh/kg). xxi Internal resistance: Expressed in ohms, the total DC resistance to the flow of current through internal components (grids, active materials, separators, electrolyte, straps, and terminal) of a cell. Module: The smallest modular unit, consisting of a number of individual cells connected together electrically in series, parallel, or both. Nominal voltage: The average voltage of the cell. The operating voltage of the system may go above or below this value. Open circuit voltage (OCV): The difference in potential between the terminals of a cell when no load is applied. Pack: Two or more modules connected in series, parallel or both. Power density: The ratio of the available power from a cell to its volume (W / L). Round-trip efficiency: The ratio of energy put in (in MWh) to energy retrieved from storage (in MWh). Self-discharge: The loss of useful capacity of a cell on storage due to internal chemical action (local action) and parasitic currents. State-of-charge (SoC): The present cell capacity in relation to maximum capacity. Terminal voltage: The difference in potential between the terminals of a cell when a load is applied.

Electric power utilities across the globe are facing higher demand for electricity than ever before, while juggling to balance environmental conservation with transmission corridor expansions. Demand side management (DSM) and dynamic thermal rating systems (DTR) play an important role in alleviating some of the challenges faced by electric power utilities. In this paper, various DSM measures are explored and their interactions with the application of the DTR system in the transmission network are examined. The proposed modelling of DSM in this paper implements load shifting on load demand curves from the system, bus and load sector levels. The correlation effects of line ratings are considered in the DTR system modelling as the weather that influences line ratings is also correlated. The modelling of the line ratings was performed using the time series method, the auto regressive moving average (ARMA) model. Both the DSM and the DTR systems were implemented on the modified IEEE reliability test network. The modification was achieved by developing a load model starting from the perspective of the load sectors at each bus and a new collective hourly load curve for the system was obtained by combining the loads at all buses. Finally, the results in this paper elucidate the interaction of DSM and DTR systems.

The goal of this paper is to discuss and compare three feature extraction methods: Linear Predictive Coefficients (LPC), Linear Prediction Cepstral Coefficients (LPCC) and Weighted Linear Prediction Cepstral Coefficients (WLPCC) for recognizing the stuttered events. Speech samples from the University College London Archive of Stuttered Speech (UCLASS) were used for our analysis. The stuttered events were identified through manual segmentation and were used for feature extraction. Two simple classifiers namely, k-nearest neighbour (kNN) and Linear Discriminant Analysis (LDA) were employed for speech dysfluencies classification. Conventional validation method was used for testing the reliability of the classifier results. The study on the effect of different frame length, percentage of overlapping, value of ã in a first order pre-emphasizer and different order p were discussed. The speech dysfluencies classification accuracy was found to be improved by applying statistical normalization before feature extraction. The experimental investigation elucidated LPC, LPCC and WLPCC features can be used for identifying the stuttered events and WLPCC features slightly outperforms LPCC features and LPC features.

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