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Power Quality Disturbance Detection Using Artificial Intelligence: A Hardware Approach
Abstract
Identification and classification of voltage and current disturbances in power systems is an important task in power system monitoring and protection. Most power quality disturbances are non-stationary and transitory and the detection and classification have proved to be very demanding. New intelligent system technologies using wavelet transform, expert systems and artificial neural networks provide some unique advantages regarding fault analysis. This paper presents new approach aimed at automating the analysis of power quality disturbances including sag, swell, transient, fluctuation, interruption and normal waveform. The approach focuses on the application of discrete wavelet transform technique to extract features from disturbance waveforms and their classification using a powerful combination of neural network and fuzzy logic. The system is modelled using VHDL followed by extensive testing and simulation to verify the correct functionality of the system. Then, the design is synthesized to APEX EP20K200EBC652-1X FPGA, tested and validated. Comparisons, verification and analysis made from the results obtained from the application of this system on software-generated and utility sampled disturbance signals validate the utility of this approach and achieved a classification accuracy of 98.17%.
... In modern world, modules are being utilized in smart home systems, advanced image processing implementations, and numerous networks [1][2][3][4][5][6][7][8]. Due to advent, of CMOS technology, semiconductor devices pervade in every discipline of engineering, and to activate these devices, oscillators become core components [9][10][11][12][13][14][15]. ...
... The trend toward increased integration of analog and digital circuitry requires data converters that can be embedded in large digital ICs [1][2][3][4][5][6][7][8]. Mixed-signal applications such as Partial Response Maximum-Likelihood (PRML) read channels and gigabit Ethernet require highspeed low-resolution ADCs, which are usually implemented with the flash architecture. ...
... The Field-Programmable Gate Arrays (FPGA) provides a potential substitute to speed up hardware implementation (Coussy et al., 2009;Marufuzzaman et al., 2010;Reaz et al., 2007a). FPGA comes with the merits of shorter design cycle, lower cost and higher density from computer-aided design perspective (Choong et al., 2005;Akter et al., 2008). It contains various building blocks. ...
Problem statement: Multiagent system is very proficient and has rules well-suited for financial forecast with its neural network. In financial forecasting, the approach for rules extractions is less pertinent and involves algorithms which are complex. The unsupervised network method lacks in comprehensibility and leads to ambiguity. Approach: The application of neural network technology to real-time processing of financial market analysis demands the development of a new processing structure which allows efficient hardware realization of the neural network mechanism. This study describes the realization of neural network on FPGA device for stock market forecasting system. The stock market forecasting neural network architecture consists of three layers. These are input layer with three neurons, hidden layer with two neurons and output layer with one neuron. For both output layer and hidden layer neurons, Sigmoid transfer function is used. Neuron of each layer is modelled individually using behavioural VHDL. The layers are then connected using structural VHDL. This is followed by timing analysis and circuit synthesis for the validation, functionality and performance of the designated circuit. The designated portfolio is then programmed through download cable into the FPGA chip. Results: Kuala Lumpur Stock Exchange (KLSE) index has been utilized for validating the usefulness of the completed prototype. Test on the sample of 100 data demonstrated an accuracy of 99.16% in predicting closing price of the KLSE index 10 days in advance. Conclusion: The test results are anticipated to be a higher rate of prediction for stock market analysis, thereby maintaining the high quality of supplying information in stock market business.
... The Field-Programmable Gate Arrays (FPGA) offers a potential substitute to accelerate the hardware implementation (Coussy et al., 2009;Marufuzzaman et al., 2010;Reaz et al., 2007b;Verma et al., 2009). FPGA has the merits of shorter design, higher density and lower cost cycle from the point of computer-aided design (Choong et al., 2005;Akter et al., 2008;ElGizawy et al., 2010). It comprises of a wide variety of building blocks. ...
Problem statement: Finite Impulse Response (FIR) filters are widely used in various DSP applications. The design of digital FIR filters is a very basic problem in digital signal processing. A FIR filter with multiple operation capability is certainly very useful for any real-time filtering applications. This article presents a multipurpose FIR filter design modeled by the hardware description language VHDL for real-time filtering application. Approach: The VHDL has its concept of concurrency to cope with the parallelism of digital hardware. The novel feature is the capability of the design to accomplish up to 127variable filter order and an arbitrary filter frequency response. The coefficients are calculated by Hamming windowing technique. Basing on selection embedded in the design, the model is able to execute highpass, lowpass, bandstop and bandpass filtering operations. It is set at 8-bit signed data processing. To filter the input data in time domain, Linear Constant Coefficient Difference Equation (LCCDE) is used by the filter. Results: The design outputs are validated through simulation and compilation. The output results are also compared with the MATLAB implemented calculated output results to test the correctness that proves the effectiveness of the design. Conclusion: With the capability of filtering signal in real time mode utilizing arbitrary filter shape, the multipurpose filter proves to be versatile.
... The Field-Programmable Gate Arrays (FPGA) offers a potential alternative to speed up the hardware realization (Marufuzzaman et al., 2010;Reaz et al., 2007b). From the perspective of computer-aided design, FPGA comes with the merits of lower cost, higher density and shorter design cycle (Choong et al., 2005). It comprises a wide variety of building blocks. ...
Problem statement: Real-time secure image and video communication is challenging due to the processing time and computational requirement for encryption and decryption. In order to cope with these concerns, innovative image compression and encryption techniques are required. Approach: In this research, we have introduced partial encryption technique on compressed images and implemented the algorithm on Altera FLEX10K FPGA device that allows for efficient hardware implementation. The compression algorithm decomposes images into several different parts. We have used a secured encryption algorithm to encrypt only the crucial parts, which are considerably smaller than the original image, which result in significant reduction in processing time and computational requirement for encryption and decryption. The breadth-first traversal linear lossless quadtree decomposition method is used for the partial compression and RSA is used for the encryption. Results: Functional simulations were commenced to verify the functionality of the individual modules and the system on four different images. We have validated the advantage of the proposed approach through comparison, verification and analysis. The design has utilized 2928 units of LC with a system frequency of 13.42MHz. Conclusion: In this research, the FPGA prototyping of a partial encryption of compressed images using lossless quadtree compression and RSA encryption has been successfully implemented with minimum logic cells. It is found that the compression process is faster than the decompression process in linear quadtree approach. Moreover, the RSA simulations show that the encryption process is faster than the decryption process for all four images tested.
... The Field-Programmable Gate Arrays (FPGA) offers a potential alternative to speed up the hardware realization (Coussy et al., 2009;. From the perspective of computer-aided design, FPGA comes with the merits of lower cost, higher density and shorter design cycle (Choong et al., 2005;Akter et al., 2008). It comprises a wide variety of building blocks. ...
Problem statement: Automated subway train-braking system require perfection, efficiency and fast response. In order to cope with this concerns, an appropriate algorithm need to be developed which need to be implemented in hardware for faster response. Approach: In this research, the FPGA realization of fuzzy based subway train braking system has been presented on an Alter FLEX10K device to provide an accurate and increased speed of convergence of the network. The fuzzy based subway train braking system is comprised of fusilier, inference, rule selector and defuzzifier modules. Sixteen rules are identified for the rule selector module. After determining the membership functions and its fuzzy variables, the Max-Min Composition method and Madman-Min implication operator are used for the inference module and the Centre of Gravity method is used for the defuzzification module. Each module is modeled individually using behavioral VHDL. The layers are then connected using structural VHDL. Two 8-bit and one 8-bit unsigned digital signals are used for input and output respectively. Six ROMs are defined in order to decrease the chances of processing and increasing the throughput of the system. Results: Functional simulations were commenced to verify the functionality of the individual modules and the system as well. We have validated the hardware implementation of the proposed approach through comparison, verification and analysis. The design has utilized 2372 units of LC with a system frequency of 139.8MHz. Conclusion: In this research, the FPGA realization of fuzzy brake system of subway train has been successfully implemented with minimum usage of logic cells. The validation study with C model shows that the hardware model is appropriate and the hardware approach shows faster and accurate response with full automatic control.
... The Field-Programmable Gate Arrays (FPGA) offers a potential alternative to speed up the hardware realization (Coussy et al., 2009;Marufuzzaman et al., 2010;Reaz et al., 2011a). From the perspective of computer-aided design, FPGA comes with the merits of lower cost, higher density and shorter design cycle (Choong et al., 2005;Akter et al., 2008;Reaz et al., 2011b). It comprises a wide variety of building blocks. ...
Problem statement: Boolean function classification plays an important role in the field like technology mapping for digital circuit design, function mapping for minimization and the development of universal logic modules. Approach: In this study, we present a single core hardware module to implement Boolean function classification techniques on Altera FLEX10K FPGA device for lossless data compression. The compression algorithm was performed by incorporating Boolean function classification into Huffman coding. This allows compression that was more efficient because the data had been categorized and simplified before the encoding was done. Simulation, timing analysis and circuit synthesis were commenced to verify the functionality and performance of the designated circuits which supports the practicality, advantages and effectiveness of the proposed single core hardware implementation. Results: The result shows a higher compression ratio. The average compression ratio was 25-37.5% from numerous testing with various text inputs with a maximum clock frequency of 27.9 MHz. Conclusion: The hardware implementation demonstrated complete, correct functionality and met all the initial system requirements.
... The Field-Programmable Gate Arrays (FPGA) offers a potential alternative to speed up the hardware realization (Coussy et al., 2009;Marufuzzaman et al., 2010;Reaz et al., 2007a). From the perspective of computer-aided design, FPGA comes with the merits of lower cost, higher density and shorter design cycle (Choong et al., 2005;Akter et al., 2008). It comprises a wide variety of building blocks. ...
Problem statement: Compression is useful because it helps reduce the consumption of expensive resources, such as hard disk space or transmission bandwidth. For effective data compression, the compression algorithm must be able to predict future data accurately in order to build a good probabilistic model for compression. Lossless compression is essential in cases where it is important that the original and the decompressed data be identical, or where deviations from the original data could be deleterious. Approach: Prediction by Partial Matching (PPM) data compression technique has utmost performance standard and capable of very good compression on a variety of data. In this research, we have introduced PPM technique to compress the data and implemented the algorithm on Altera FLEX10K FPGA device that allows for efficient hardware implementation. The PPM algorithm was modeled using the hardware description language VHDL. Results: Functional simulations were commenced to verify the functionality of the system with both 16-bit input and 32-bit input. The FPGA utilized 1164 logic cells with a maximum system frequency of 95.3MHz on Altera FLEX10K. Conclusion: The proposed approach is computationally simple, accurate and exhibits a good balance of flexibility, speed, size and design cycle time.
... The Field-Programmable Gate Arrays (FPGA) provides a potential substitute to speed up hardware implementation (Coussy et al., 2009;Marufuzzaman et al., 2010;Reaz et al., 2007a). FPGA comes with the merits of shorter design cycle, lower cost and higher density from computer-aided design perspective (Choong et al., 2005;Akter et al., 2008). It contains various building blocks. ...
Problem statement: Multiagent system is very proficient and has rules well-suited for financial forecast with its neural network. In fina ncial forecasting, the approach for rules extractio ns is less pertinent and involves algorithms which are co mplex. The unsupervised network method lacks in comprehensibility and leads to ambiguity. Approach: The application of neural network technology to real-time processing of financial market analysis d emands the development of a new processing structure which allows efficient hardware realizati on of the neural network mechanism. This study describes the realization of neural network on FPGA device for stock market forecasting system. The stock market forecasting neural network architectur e consists of three layers. These are input layer with three neurons, hidden layer with two neurons a nd output layer with one neuron. For both output layer and hidden layer neurons, Sigmoid transfer fu nction is used. Neuron of each layer is modelled individually using behavioural VHDL. The layers are then connected using structural VHDL. This is followed by timing analysis and circuit synthesis f or the validation, functionality and performance of the designated circuit. The designated portfolio is then programmed through download cable into the FPGA chip. Results: Kuala Lumpur Stock Exchange (KLSE) index has been utilized for validating the usefulness of the completed prototype. Test on the sample of 100 data demonstrated an accuracy of 99.16% in predicting closing price of the KLSE inde x 10 days in advance. Conclusion: The test results are anticipated to be a higher rate of pred iction for stock market analysis, thereby maintaini ng the high quality of supplying information in stock market business.
... Modularity is employed with WT-MNN and eight nodes in hidden layer for each module was found suitable with learning rate 0.5 having less iteration as given by training results in Table 3. Table 3 Training results for WT-MLNN and WT-MNN Training time of MNN is reduced as each module is trained for its corresponding disturbance class. Architecture with FPGA can be realised (Huang et al., 2002;Choong et al., 2005) with parallel data processing for detection and classification. WT-MNN modules are parallel trained and maximum learning time of network is dependent on module requiring more number of iterations. ...
Disturbances such as voltage sag, swell, interruption and harmonics are very typical in a power system. Power quality monitoring should be capable of identifying these disturbances to initiate mitigation action and protect sensitive loads. This paper presents wavelet-neural network-based detection and classification of power quality disturbances. Wavelet transform has the ability to analyse signals simultaneously in both time and frequency domains and is used to extract features of the disturbances by decomposing the signal using multi resolution analysis. These features, used to detect and localise the disturbances and are not easily separable, will reduce the performance of multilayer neural network. Improvement in the classification accuracy is suggested by employing modular neural network obtained by dividing a complex task into easier subtasks. The algorithm proposed is tested for classification of various power quality disturbances and it is found that a modular neural network has a higher classification accuracy over traditional multilayer neural network.
This thesis focuses on simulating, detecting, localizing and classifying the power quality disturbances using advanced signal processing techniques and neural networks. Primarily discrete wavelet and Fourier transforms are used for feature extraction, and classification is achieved by using neural network algorithms. The proposed feature vector consists of a combination of features computed using multi resolution analysis and discrete Fourier transform. The proposed feature vectors exploit the benefits of having both time and frequency domain information simultaneously. Two different classification algorithms based on Feed forward neural network and adaptive resonance theory neural networks are proposed for classification. This thesis demonstrates that the proposed methodology achieves a good computational and error classification efficiency rate.
This paper describes the design and modeling of an artificial neural network (ANN) classifier using VHDL. This classifier is targeted primarily to classify the six different types of power quality disturbance. The high level architecture comprises of a control unit and a neural network datapath. The control unit is further divided into five interconnected sub modules: bus master, ram, pseudo random number generator, error calculator and trainer. Univariate randomly optimized Neural Network (uronn) algorithm is employed to model the neural network. Proper simulation is carried out to verify the functionality of the individual modules and the system. In addition, the algorithm was also implemented in Matlab and C as comparison with the hardware implementation in VHDL. Comparisons, verification and analysis made validate the advantage of this approach. Currently, the classification average accuracy is 77.53%. The classifier also has the potential of being extended to classify other kinds of power quality disturbances.
Evaluates the predictability of BP, GA and combined GA&BP in
terms of control program design, convergence behaviour, learning
capability and harmonic compensation efficacies, as applied to the
filtering of load generated current harmonics in a DC variable-speed
drive. Using the same measured current harmonic data, our performance
evaluations confirm that the BP has a faster learning convergence
whereas GA provides a more robust control system. A Combined BP and GA
algorithm offers good reduction in harmonic content and thus improves
the quality of power supply
The wavelet transform is introduced as a powerful tool for monitoring power quality problems generated due to the dynamic performance of industrial plants. The paper presents a multiresolution signal decomposition technique as an efficient method in analyzing transient events. The multiresolution signal decomposition has the ability to detect and localize transient events and furthermore classify different power quality disturbances. It can also be used to distinguish among similar disturbances.
This paper presents an approach for detection and classification of power quality disturbances using wavelet transform, fuzzy logic and neural network. The total harmonic distortion (THD) and energy of the disturb signals are used for classification. A maiden attempt is made to apply a new tool called neuro solution for artificial neural network (ANN) in the field of power quality disturbance classification. A comparison of fuzzy logic and neural network for disturbance classification has been made. Comparison of these two techniques reveals that ANN is more accurate and efficient than the fuzzy logic.
“Power quality problems have increasingly become a substantial concern over the last decade, but surprisingly few analytical techniques have been developed to overcome these disturbances in system-equipment interactions. Now in this comprehensive book, power engineers and students can find the theoretical background necessary for understanding how to analyze, predict, and mitigate the two most severe power disturbances: voltage sags and interruptions. This is the first book to offer in-depth analysis of voltage sags and interruptions and to show how to apply mathematical techniques for practical solutions to these disturbances. From UNDERSTANDING AND SOLVING POWER QUALITY PROBLEMS you will gain important insights into Various types of power quality phenomena and power quality standards Current methods for power system reliability evaluation Origins of voltage sags and interruptions Essential analysis of voltage sags for characterization and prediction of equipment behavior and stochastic prediction Mitigation methods against voltage sags and interruptions.
The analysis of transmission line faults is essential to the
proper performance of a power system. It is required if protective
relays are to take appropriate action and in monitoring the performance
of relays, circuit breakers and other protective and control elements.
The detection and classification of transmission line faults is a
fundamental component of such fault analysis. Here, the authors describe
how a neural network, trained to recognize patterns of transmission line
faults, has been incorporated in a PC-based system that analyzes data
files from substation digital fault recorders
Realization of wavelet transform on field-programmable gate array
(FPGA) device for the detection of power system disturbances is proposed
in this paper. This approach provides an integral signal-processing
paradigm, where its embedded wavelet basis serves as a window function
to monitor the signal variations very efficiently. By using this
technique, the time information and frequency information can be unified
as a visualization scheme, facilitating the supervision of electric
power signals. To improve its computation performance, the proposed
method starts with the software simulation of wavelet transform in order
to formulate the mathematical model. This is followed by the circuit
synthesis and timing analysis for the validation of the designated
circuit. Then, the designated portfolio can be programmed into the FPGA
chip through the download cable. And the completed prototype will be
tested through software-generated signals and utility-sampled signals,
in which test scenarios covering several kinds of electric power quality
disturbances are examined thoroughly. From the test results, they
support the practicality and advantages of the proposed method for the
applications
This paper presents a neural-fuzzy technology-based classifier for
the recognition of power quality disturbances. The classifier adopts
neural networks in the architecture of frequency sensitive competitive
learning and learning vector quantization (LVQ). With given size of
codewords, the neural networks are trained to determine the optimal
decision boundaries separating different categories of disturbances. To
cope with the uncertainties in the involved pattern recognition, the
neural network outputs, instead of being taken as the final
classification, are used to activate the fuzzy-associative-memory (FAM)
recalling for identifying the most possible type that the input waveform
may belong to. Furthermore, the input waveforms are preprocessed by the
wavelet transform for feature extraction so as to improve the classifier
with respect to recognition accuracy and scheme simplicity. Each subband
of the transform coefficients is then utilized to recognize the
associated disturbances
This paper presents a novel classification method for power
distribution line disturbances using a rule-based method and a wavelet
packet-based hidden Markov model (HMM). The rule-based method is
utilized for the classification of time-characterized-feature
disturbances, and the wavelet packet-based HMM is utilized for the
frequency-characterized-feature power disturbances. This proposed method
classifies six types of actual recorded power distribution disturbances,
i.e., sag, interruption, fast capacitor switching, capacitor switching,
normal variation, and impulse disturbance, and obtains 98.7% correct
classification rate for 670 actual disturbance events tested
For pt.I see ibid., vol.15, no.1, p.222-8 (2000). A wavelet-based
neural classifier is constructed and thoroughly tested under various
conditions, The classifier is able to provide a degree of belief for the
identified waveform. The degree of belief gives an indication about the
goodness of the decision made. It is also equipped with an acceptance
threshold so that it can reject ambiguous disturbance waveforms. The
classifier is able to achieve the accuracy rate of more than 90% by
rejecting less than 10% of the waveforms as ambiguous
The wavelet transform is introduced as a method for analyzing
electromagnetic transients associated with power system faults and
switching. This method, like the Fourier transform, provides information
related to the frequency composition of a waveform, but it is more
appropriate than the familiar Fourier methods for the nonperiodic,
wide-band signals associated with electromagnetic transients. It appears
that the frequency domain data produced by the wavelet transform may be
useful for analyzing the sources of transients through manual or
automated feature detection schemes. The basic principles of wavelet
analysis are set forth, and examples showing the application of the
wavelet transform to actual power system transients are presented
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