Joy Mazumdar

Intelligent Tool for Determining the True Harmonic Current Contribution of a Customer in a Power Distribution Network

Customer loads connected to electricity supply systems may be broadly categorized as either linear or nonlinear. Nonlinear loads inject harmonics into the power network. Harmonics in a power system are classified as either load harmonics or as supply harmonics depending on their origin. The source impedance also impacts the harmonic current flowing in the network. Hence any change in the source impedance is reflected in the harmonic spectrum of the current. This paper proposes a novel method based on artificial neural networks to isolate and evaluate the impact of the source impedance change without disrupting the operation of any load, by using actual field data. The test site chosen for this study has a significant amount of triple harmonics in the current. By processing the acquired data with the proposed algorithm, the actual load harmonic contribution of the customer is predicted. Experimental results confirm that attempting to predict the total harmonic distortion (THD) of a customer by simply measuring the customers current may not be accurate. The main advantage of this method is that only waveforms of voltages and currents at the point of common coupling have to be measured. This method is applicable for both single and three phase loads

Synchronous Reference Frame Based Active Filter Current Reference Generation Using Neural Networks

The increased use of nonlinear devices in industry has resulted in direct increase of harmonic distortion in the industrial power system in recent years. The significant harmonics are almost always 5th, 7th, 11th and the 13th with the 5th harmonic being the largest in most instances. Active filter systems have been proposed to mitigate harmonic currents of the industrial loads. The most important requirement for any active filter is the precise detection of the individual harmonic components amplitude and phase. Fourier transform based techniques provide an excellent method for individual harmonic isolation, but it requires a minimum of two cycles of data for the analysis, does not perform well in the presence of subharmonics which are not integral multiples of the fundamental frequency and most importantly introduces phase shifts. To overcome these difficulties, this paper proposes a multilayer perceptron neural network trained with back-propagation training algorithm to identify the harmonic characteristics of the nonlinear load. The operation principle of the synchronous-reference-frame-based harmonic isolation is discussed. This proposed method is applied to a thyristor controlled DC drive to obtain the accurate amplitude and phase of the dominant harmonics. This technique can be integrated with any active filter control algorithm for reference generation

System and method for determining harmonic contributions from non-linear loads

Neural networks have been known to be good function approximators. They are particularly effective in dealing with non-linear relationships between parameters. This feature is exploited in this paper to propose a new method for the problem of measuring harmonic current injected into a power system network by a non-linear load without disconnecting the load from the network. This work is particularly useful in determining whether the utility or the customer side has a higher contribution to harmonic pollution in a network. Hence this method would be helpful in settling utility-customer disputes over who is responsible for harmonic distortions. The main advantage of this method is that only waveforms of voltages and currents have to be measured. A neural network structure with memory is used to identify or learn the non-linear load admittance of a load. Once it has learned the admittance, the neural network predicts the true harmonic current of the load when supplied with a clean sine wave. This method is applicable for both single and three phase loads. This could be fabricated into a commercial instrument that could be installed in substations of large customer loads, or used as a hand-held clip on instrument.

Utilization of Supplementary Energy Storage Systems in High Power Mining Converters

This paper proposes the utilization of ultracapacitor as additional energy storage device in high power AC mining converters. A critical requirement for these converters is to be able to maintain a robust DC link under all operating conditions. However, due to lower switching frequencies of the IGBT devices, the bandwidth of the controllers is limited. As a result, during regenerative periods, the DC link voltage may have a tendency to increase. Protective devices like choppers and crowbars are added to the system. Integration of the ultracapacitor system to the converter can aid the recovery and storage of the regenerative energy and reutilize that energy for meeting the systems power demand. This will improve the overall performance of the system and reduce the dependence on the chopper and crowbar circuits. Implementation of this system translates to power quality improvement of the main grid.

Predicting load harmonics in three phase systems using neural networks

This paper proposes a artificial neural network (ANN) based method for the problem of measuring the actual harmonic current injected into a power system network by three phase nonlinear loads without disconnecting any loads from the network. The ANN directly estimates or identifies the nonlinear admittance (or impedance) of the load by using the measured values of voltage and current waveforms. The output of this ANN is a waveform of the current that the load would have injected into the network if the load had been supplied from a sinusoidal voltage source and is therefore a direct measure of load harmonics

Using a Neural Network to Distinguish Between the Contributions to Harmonic Pollution of Non-Linear Loads and the Rest of the Power System

Harmonics are one of the important power quality measurable quantities. This paper proposes a neural network solution methodology for the problem of measuring the actual amount of harmonic current injected into a power network by a non-linear load. The determination of harmonic currents is complicated by the fact that the supply voltage waveform is distorted by other loads and is rarely a pure sinusoid. Harmonics may therefore be classified as contributions from the load on the one hand and contributions from the power system or supply harmonics on the other hand. A recurrent neural network architecture based method is used to find a way of distinguishing between the load contributed harmonics and supply harmonics, without disconnecting the load from the network. The main advantage of this method is that only waveforms of voltages and currents have to be measured. This method is applicable for both single and three phase loads. This could be fabricated into a commercial instrument that could be installed in substations of large customer loads, or used as a hand-held clip on instrument

System and method for determining harmonic contributions from non-linear loads using recurrent neural networks

This paper proposes a neural network solution methodology for the problem of measuring the actual amount of harmonic current injected into a power network by a non-linear load. The determination of harmonic currents is complicated by the fact that the supply voltage waveform is distorted by other loads and is rarely a pure sinusoid. Harmonics may therefore be classified as contributions from the load on the one hand and contributions from the power system or supply harmonics on the other hand. A recurrent neural network architecture based method is used to find a way of distinguishing between the load contributed harmonics and supply harmonics, without disconnecting the load from the network. The main advantage of this method is that only waveforms of voltages and currents have to be measured. This method is applicable for both single and three phase loads. This could be fabricated into a commercial instrument that could be installed in substations of large customer loads, or used as a hand-held clip on instrument.

Determining IEEE 519 Compliance of a Customer in a Power System

Nonlinear loads inject harmonics in a power distribution network. The interaction of the nonlinear load harmonics with the network impedances creates voltage distortions at the point of common coupling (PCC) which in turn affects other loads connected to the same PCC. This paper presents a neural network based technique to establish determine the true current harmonic distortion of a customer in the presence of distorted voltage. The proposed method is applied at the primary metering point of a customer in a distribution network and the results are used to establish compliance with IEEE 519.

Monitoring the True Harmonic Current of a Variable Speed Drive Under Nonsinusoidal Supply Conditions

The increased use of nonlinear devices in industry has resulted in direct increase of harmonic distortion in the industrial power system. Variable speed drives are an example. With the widespread proliferation of nonlinear loads in a power distribution network, the voltage at the point of common coupling is rarely a pure sinusoid. It has become necessary to identify accurately which load(s) is injecting the excessively high harmonic currents. Simply measuring the harmonic currents at each individual load is not sufficiently accurate since these harmonic currents may be caused by not only the nonlinear load, but also by a non-sinusoidal PCC voltage. This paper proposes a neural network solution methodology for the problem of measuring the actual amount of harmonic current injected into a power network by a three phase variable speed drive, and this technique can be extended to any nonlinear load in general. The proposed method has been experimentally verified by applying the scheme to a commercially available variable speed drive. The scheme has been applied to each phase individually as well as to all three phases together. The goal of this paper is to quantify the difference in current distortion of a load when supplied from a distorted source as compared to a clean sine wave. A Multilayer Perceptron Neural Network is used to estimate the true harmonic current distortion of a load. Theory and practical results are presented. This technology could be integrated into any commercially available power quality instrument or be fabricated as a standalone instrument.

Identifying Harmonic Contributions from Non-Linear Loads using Neural Networks

This paper proposes a neural network solution methodology for the problem of measuring the actual amount of harmonic current injected into a power network by a non-linear load. The determination of harmonic currents is complicated by the fact that the supply voltage waveform is distorted by other loads and is rarely a pure sinusoid. A recurrent neural network architecture based method is used to find a way of distinguishing between the load contributed harmonics and supply harmonics, without disconnecting the load from the network. The main advantage of this method is that only waveforms of voltages and currents have to be measured. This method is applicable for both single and three phase loads. This could be fabricated into a commercial instrument that could be installed in substations of large customer loads, or used as a hand-held clip on instrument