There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

On robust estimation of the location parameter

In the world, wind power is rapidly becoming a generation technology of significance. Unpredictability and variability of wind power generation is one of the fundamental difficulties faced by power system operators. Good forecasting tools are urgent needed under the relevant issues associated with the integration of wind energy into the power system. This paper gives a bibliographical survey on the general background of research and developments in the fields of wind speed and wind power forecasting. Based on the assessment of wind power forecasting models, further direction for additional research and application is proposed.

A review on the forecasting of wind speed and generated power

http://pierrepinson.com/31761/Literature/foley2012.pdf

Current methods and advances in forecasting of wind power generation

In this paper we present a new approach to detect, localize, and investigate the feasibility of classifying various types of power quality disturbances. The approach is based on wavelet transform analysis, particularly the dyadic-orthonormal wavelet transform. The key idea underlying the approach is to decompose a given disturbance signal into other signals which represent a smoothed version and a detailed version of the original signal. The decomposition is performed using multiresolution signal decomposition techniques. We demonstrate and test our proposed technique to detect and localize disturbances with actual power line disturbances. In order to enhance the detection outcomes, we utilize the squared wavelet transform coefficients of the analyzed power line signal. Based on the results of the detection and localization, we carry out an initial investigation of the ability to uniquely characterize various types of power quality disturbances. This investigation is based on characterizing the uniqueness of the squared wavelet transform coefficients for each power quality disturbance.

Power quality assessment via wavelet transform analysis

This paper presents a stand-alone dc-bus Electric Vehicle (EV) charging station system using a photovoltaic (PV) source. The proposed topology includes a PV panel, an energy storage unit (ESU), and two EVs. Each unit is controlled independently; a voltage regulation scheme is implemented within the ESU converter to ensure the dcbus voltage is maintained at the nominal level under varying operating conditions. Two different operating scenarios are studied, and the results confirm that the algorithm developed allows regulating the dcbus voltage either with varying solar irradiance or when EV s are connected and disconnected from the dc grid. The proposed design offers lower energy conversion losses compared to the ac configuration and reduces energy demand on the existing ac distribution grid. It also finds applications in remote areas where parking facilities are provided for recreational purpose.

Design and Control of a Photovoltaic-Fed Dc-Bus Electric Vehicle Charging Station

Battery energy storage systems are often controlled through an energy management system (EMS), which may not have access to detailed models developed by battery manu-facturers. The EMS contains a model of the battery system’s performance capabilities that enables it to optimize charge and discharge decisions. In this paper, we develop a process for the EMS to calculate and improve the accuracy of its control model using the operational data produced by the battery system. This process checks for data salience and quality, identifies candidate parameters, and then calculates their accuracy. The process then updates its model of the battery based on the candidate parameters and their accuracy. We use a charge reservoir model with a first order equivalent circuit to represent the battery and a flexible open-circuit-voltage function. The process is applied to one year of operational data from two lithium-ion batteries in a battery system located in Sterling, MA USA. Results show that the process quickly learns the optimal model parameters and significantly reduces modeling uncertainty. Applying this process to an EMS can improve control performance and enable risk-averse control by accounting for variations in capacity and efficiency.

Adaptive Modeling Process for a Battery Energy Management System

Impedance-based algorithms commonly used for determining the fault location in transmission lines are prone to several sources of error and are specific to the line and system configuration. Furthermore, these algorithms do not utilise available valuable information about the power system surrounding the faulted line. These issues can be overcome using a model-based fault location (MBFL) approach. It uses a circuit model to simulate possible fault scenarios and compares the simulated fault currents with the measured currents recorded by the relay to identify the fault location. However, there are several difficulties and limitations while applying MBFL. There is a loss in accuracy and precision based on the number of simulated scenarios and a requirement to store voluminous simulation results. Hence, this study presents a novel application technique for implementing model-based approach efficiently to estimate the fault location and fault resistance using artificial neural networks-based approach. A key highlight of the proposed approach is the ability to identify the location of a fault present on neighbouring lines using the measured through fault current. The study also presents representative scenarios to demonstrate the capability and potential of the proposed approach.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-stg.2019.0135

Application technique for model-based approach to estimate fault location

Power-quality voltage and current transient waveform data have been explored rather extensively as the primary input data in predictive maintenance, automatic root-cause analysis, and evaluating system performance to indicate potential problems. Unfortunately, very few efforts have been directed toward making use of the voluminous steady-state data collected alongside waveform data. Therefore, this paper proposes to use steady-state data, particularly, rms voltage data to detect abnormal trend behavior that may be indicative of a problem. Specifically, this paper develops a statistical analysis algorithm based on the well-known statistical process control methods for assessing feeder voltage regulation performance. The assessment results can be used to indicate potential regulator problems as well. The efficacy of the method is demonstrated by applications to two sets of actual RMS voltage data.

Assessment of Feeder Voltage Regulation Using Statistical Process Control Methods

Mitigating wind power variability is essential to improving grid reliability and power dispatch flexibility. The objective of this paper is to determine the optimal size of energy storage systems (ESSs) using the proposed conditional range metric (CRM). By employing a two-parameter gamma distribution, intra-hour wind power variability can be quantified via its cumulative distribution functions (CDF) at each production level. A maximum likelihood estimator delivers accurate parameters to the gamma CDF. To reduce intra-hour power variations, a generic ESS is incorporated with the wind farm. Optimal designs for sizing and dispatch schemes of the ESS are proposed using wind power time-series available from the National Renewable Energy Laboratory. Validity and efficacy of these designs are evaluated and quantified by the CRM.

Surya Santoso

Papers

Design and Control of a Photovoltaic-Fed Dc-Bus Electric Vehicle Charging Station

Adaptive Modeling Process for a Battery Energy Management System

Application technique for model-based approach to estimate fault location

Assessment of Feeder Voltage Regulation Using Statistical Process Control Methods

Determining optimal energy storage size to mitigate intra-hour wind power variability