Harmonic distortion analysis in power quality signal using time-frequency distribution

Harmonic distortion in the electrical power supply is caused by an increase in the number of power electronics devices. Harmonic distortion may have an effect on the production process, as well as economic losses and equipment failure. As a result, it is important to detect harmonic signals, identify, and to diagnose type of harmonic source in order to take precautionary measures to avoid the negative effects of harmonic distortion. Mostly, the power quality (PQ) analysis only focuses on the harmonic signal measurement, whereas it is also necessary to identify the location and type of harmonic sources with low complexity and high accuracy capability. Therefore, this research presents PQ signal analysis, detection, harmonic source identification and diagnosis method. The power quality signals consist of multi-frequency components and magnitude differences, thus, the time-frequency distribution (TFD) is very suited to present the signals within time-frequency representation (TFR) and to detect power quality signals accurately. The TFDs namely spectrogram, Gabor transform and S-transform are used in this study. The signal parameters are estimated and then are used to identify the signal characteristics based on the IEEE Standard 1159-2019. The best TFD in harmonic signal detection is identified in regards to the accuracy, calculation complexity, and memory size of signal analysis. Next, using the best TFD, the harmonic source is identified either from downstream and/or upstream of the point of common coupling (PCC) based on impedance spectral. Afterwards, five machine learning methods include k-nearest neighbour (KNN), support vector machine with linear function (SVM-L), support vector machine with radial basis function (SVM-RBF), linear discriminate analysis (LDA) and naïve Bayes (NB) are used to diagnose the harmonic sources. Three harmonic signal parameter groups which are harmonic voltage parameters, harmonic current parameters, and harmonic voltage and current parameters are examined. The performance of the detection method is verified by generating and detecting 100 multiple characteristics signals for each type of power quality signal. Meanwhile, 100 signals of harmonic sources, which are from rectifier and inverter loads with various characteristics in terms of firing angle, amplitude and frequency modulation indexes are evaluated in identification and diagnosis of the harmonic source method. The diagnosis results indicate that the LDA with harmonic voltage parameters offer the highest accuracy and fastest computation speed. To validate the proposed method, the real signals of field testing were recorded and analysed for detection, identification, and diagnosis methods. The results show that the proposed method provides high accuracy and fast computational analysis, making it ideal for use with an embedded device in detecting power quality signals, identifying, and diagnosing harmonic sources. The proposed method gives high-impact to the industry especially in reducing maintenance cost, and trouble-shoot duration of power system failure

POWER QUALITY SIGNALS DETECTION AND CLASSIFICATION USING LINEAR TIME FREQUENCY DISTRIBUTION

Power quality has become a great concern to all electricity consumers. Poor quality can cause equipment failure, data and economical. An automated monitoring system is needed to ensure signal quality, reduces diagnostic time and rectifies failures. This paper presents the detection and classification of power quality signals using linear timefrequency distributions (TFD). The power quality signals focus on swell, sag, interruption, transient, harmonic, interharmonic and normal voltage based on IEEE Std. 1159-2009. The time-frequency analysis techniques selected are spectrogram and Gabor transform to represent the signals in time-frequency representation (TFR). From the time frequency representation (TFR) obtained, the signal parameters are estimated to identify the signal characteristics. The signal characteristics are the average of root means square voltage (Vave,rms), total waveform distortion (TWD), total harmonic distortion (THD) and total non harmonic distortion (TnHD) and duration of swell, sag, interruption and transient signals will be used as input for signals classification. The results show that spectrogram with the half window shift (HWS) provides better performance in term of accuracy, memory size, and computation complexit

A New Two Points Method for Identify Dominant Harmonic Disturbance Using Frequency and Phase Spectrogram

This paper is focused on a practical new method for dominant harmonic disturbance detection implemented using phase and frequency spectrogram based on two-point method. The first measurement point is measured at the incoming of the point of common coupling while the second measurement point at the incoming of the load. After that, the data is processed with phase and frequency spectrogram. By comparing the data, the dominant harmonic disturbance can be identified clearly. The proposed method is compared with power direction method which is the earliest method normally used in commercial product. Then, simulation and experiment are conducted to verify the accuracy of the proposed method. Finally, the results show the proposed method is more accurate than power direction method. Further work is needed to investigate the performance of the proposed method by field measurement

Milestone of the most used maximum power point tracking in solar harvesting system

Solar harvesting system with photovoltaic (PV) is one of the most desirable renewable energy sources because of its prominent advantages. However, low efficiency due to fluctuating output power is a major problem for PV systems. A technique used to maximize power extraction known as maximum power point tracking (MPPT) has been proposed by various literature to deal with this problem. One of the most widely developed MPPT methods due to its ease of implementation is perturb and observe (P&O). Since the initial discovery of the principle, the P&O method has been extensively modified including the fixed step-size: step-size variables, partial shading, threshold module current, three-point-comparison, maximization of dynamic performance, minimization of dynamic performance, bandwidth o

Power Quality Signals Detection Using S-Transform

Power quality has become very important issue over the last decade. Poor quality can cause equipment failure, data and economical. An automated monitoring system is needed to ensure signal quality, reduces diagnostic time and rectifies failures. In this paper, S-transform is used to analyze the power quality signals such as swell, sag, interruption, harmonic, interharmonic and transient based on IEEE Std. 1159-2009 to detect, localize and classify the disturbance. The S-transform is used to represent the signals in time-frequency representation (TFR). ).To get an accurate TFR, the parameters are estimated to identify the signal characteristics. The signal characteristics are the root means square voltage (Vrms), total harmonic distortion (THD), total non harmonic distortion (TnHD) and total waveform distortion (TWD). To verify the performance of Stransform several sets of data with different time duration are analyzed to determine the accuracy of S-transform. The lowest value of mean absolute percentage error (MAPE) gives the highest accuracy to provide the best performance of TFD

An Accurate Classification Method Of Harmonic Signals In Power Distribution System By Utilising S-Transform

This paper presents an accurate classification method of harmonic signal in power distribution system by using S-transform (ST). ST has a capability of representing signals in jointly time-frequency domain and known as time frequency representation (TFR). The spectral parameters are estimated from TFR in order to identify the characteristics and to classify the harmonic signals. The classification of harmonic signals with the utilization of pattern recognition approach which is rule-based classifier of 100 unique signals is according to the IEEE standard 519:2014. The accuracy of the proposed method is determined by using MAPE and the results proved that the method provides high accuracy of harmonic signal classification. Additionally, S-transform also gives 100 percent correct classification of harmonic signals. It is proven that the proposed method is accurate in detecting and classifying harmonic signals in the distribution system

Internet of things-based photovoltaics parameter monitoring system using NodeMCU ESP8266

The use of the internet of things (IoT) in solar photovoltaic (PV) systems is a critical feature for remote monitoring, supervising, and performance evaluation. Furthermore, it improves the long-term viability, consistency, efficiency, and system maintenance of energy production. However, previous researchers' proposed PV monitoring systems are relatively complex and expensive. Furthermore, the existing systems do not have any backup data, which means that the acquired data could be lost if the network connection fails. This paper presents a simple and low-cost IoT-based PV parameter monitoring system, with additional backup data stored on a microSD card. A NodeMCU ESP8266 development board is chosen as the main controller because it is a system-on-chip (SOC) microcontroller with integrated Wi-Fi and low-power support, all in one chip to reduce the cost of the proposed system. The solar irradiance, ambient temperature, PV output voltage and PV output current, are measured with photo-diodes, DHT22, impedance dividers and ACS712. While, the PV output power is a product of the PV voltage and PV current. ThingSpeak, an open-source software, is used as a cloud database and data monitoring tool in the form of interactive graphics. The results showed that the system was designed to be highly accurate, reliable, simple to use, and low-cost

A new vector draft method for harmonic source detection at point of common coupling

In modern power networks, the issue of power quality (PQ) is becoming very important because of the increasing of load which sensitive to current disturbances. This is mainly due to the increasing use of non-linear power electronic devices draws nonsinusoidal current and creating a current distortion. As a result there is increasing need for PQ to be monitored to establish the type, sources and locations of PQ disturbances, allowing remedial measures to be taken. Consequently, harmonic is one of the most concerned power quality disturbances. The detection of harmonic source is necessary for power quality strategy development. This paper introduces a new single-point measurement method to estimate the harmonic source by using phase spectrogram (PS) and frequency spectrogram (FS) based on a vector draft method. A measurement at the point of common coupling (PCC) with harmonic distortion is done by simulation via PSCAD. Then PSCAD’s data are analyzed by using spectrogram in MATLAB. To be precise, voltage and current waveforms are normalized with fundamental magnitude respectively. Next, the normalized voltage and current are plotted on the vector draft to estimate the perpendicular point between the vectors. The center point of the normalized voltage is the boundary between downstream and upstream. The harmonic source can be detected base on the perpendicular point’s location that fall on the particular region. The comparison between actual and power direction result have been conducted. Finally, the proposed method is similar with the actual result and more truthful than power direction method

An improved smooth-windowed Wigner-Ville distribution analysis for voltage variation signal

This paper outlines research conducted using bilinear time-frequency distribution (TFD), a smooth-windowed wigner-ville distribution (SWWVD) used to represent time-varying signals in time-frequency representation (TFR). Good time and frequency resolutions offer superiority in SWWVD to analyze voltage variation signals that consist of variations in magnitude. The separable kernel parameters are estimated from the signal in order to get an accurate TFR. The TFR for various kernel parameters is compared by a set of performance measures. The evaluation shows that different kernel settings are required for different signal parameters. Verification of the TFD that operated at optimal kernel parameters is then conducted. SWWVD exhibits a good performance of TFR which gives high peak-to-side lobe ratio (PSLR) and signal-to-cross-terms ratio (SCR) accompanied by low main-lobe width (MLW) and absolute percentage error (APE). This proved that the technique is appropriate for voltage variation signal analysis and it essential for development in an advanced embedded system