Electric power utilities must ensure a consistent and undisturbed supply of power, with the voltage levels adhering to specified ranges. Any deviation from these supply specifications can lead to malfunctions in equipment. Monitoring the quality of supplied power is crucial to minimize the impact of fluctuations in voltage. Variations in voltage or current from their ideal values are referred to as "power quality (PQ) disturbances," highlighting the need for vigilant monitoring and management. Signal processing methods are widely used for power system applications which include understanding of voltage disturbance signals and used for retrieval of signal information from the signals Different signal processing methods are used for extracting information about a signal. The method of Fourier analysis involves application of Fourier transform giving frequency information. The method of Short-Time Fourier analysis involves application of Short-Time Fourier transform (STFT) giving time–frequency information. The method of continuous wavelet analysis involves application of Continuous Wavelet transform (CWT) giving signal information in terms of scale and time where frequency is inversely related to scale. The method of discrete wavelet analysis involves application of Discrete Wavelet transform (DWT) giving signal information in terms of approximations and details where approximations and details are low and high frequency representation of original signal. In this paper, an attempt is made to perceive power quality disturbances in MATLAB using Fourier, Short-Time Fourier, Continuous Wavelet and Discrete Wavelet Transforms. Proper understanding of the signals can be possible by transforming the signals into different domains. An emphasis on application of signal processing techniques can be laid for power quality studies. The paper compares the results of each transform using MATLAB-based visualizations. The discussion covers the advantages and disadvantages of each technique, providing valuable insights into the interpretation of power quality disturbances. As the paper delves into the complexities of each method, it takes the reader on a journey of signal processing complexities, culminating in a nuanced understanding of power quality disturbances and their representations across various domains. The outcomes of this research, elucidated through energy values, 3D plots, and comparative analyses, contribute to a comprehensive understanding of power quality disturbances. The findings not only traverse theoretical domains but also find practical utility in real-world scenarios.

Power quality (PQ) disturbances, such as voltage sags, are significant issues that can lead to damage in electrical equipment and system downtime. Detecting and classifying these disturbances accurately is essential for maintaining reliable power systems. This paper introduces a novel approach to voltage sag analysis by employing wavelet packet analysis combined with energy-based feature extraction to enhance PQ monitoring. The study decomposes voltage sag signals into different frequency bands to extract key features for disturbance detection. We compare six commonly used mother wavelets (db1, db4, db10, dmey, sym5, and coif5) to identify the most suitable wavelet for voltage sag detection. The energy distribution curve analysis is used to evaluate the energy characteristics of each wavelet’s decomposition, with a focus on identifying the most effective signal features for PQ monitoring. The paper presents a thorough error analysis and compares the energy values extracted by different wavelet functions to demonstrate the reliability and accuracy of the proposed method. The results show that wavelet packet analysis significantly improves the detection and classification of voltage sag disturbances, providing a robust and efficient tool for real-time PQ monitoring. This study contributes to the development of advanced PQ monitoring systems by offering a more precise and computationally efficient method for voltage sag analysis, ultimately helping to protect electrical systems from potential damage and reducing operational costs. Wavelet packet analysis is applied as a novel feature extraction method for voltage sag detection by offering improved time-frequency analysis over traditional methods. Energy features are extracted from wavelet packet coefficients for sag identification. This work utilizes wavelet packet analysis to extract energy-based features for voltage sag detection.

The nonlinear characteristics of power electronic-based loads in a power system contribute a major role in the harmonics’ injection and other power quality disturbances. This affects the quality of the power supplied to consumers by distribution systems. Flexible AC transmission system (FACTS) devices at the transmission level and custom power devices at the distribution level are used for an effective power transfer. In addition to these devices, filters play a prominent role in distribution systems. This paper aims to analyze the supply voltage in the time–frequency domain to perceive the variations in signal strength, explaining the role and significance of filters in mitigating or reducing harmonics. This paper presents the significance of harmonic filters in terms of signal-processing terminology. This depicts the prominent role of filters in improving the power quality by harmonic reduction or elimination, depending upon the requirement. The mathematical transform used in this approach is the short-time Fourier transform, which results in transforming the signal into a domain giving information about time and frequency. A MATLAB Simulink environment and ‘spectrogram’ were used to simulate harmonic signals, and we analyzed them using a short-time Fourier transform. Different windows were used with varying window size lengths.

Unwanted variations in the voltage that electric power companies provide include transient, sag, swell, interruption, voltage imbalance, DC offset, harmonics, noise, and fluctuations. Since constant voltage must be provided, the quality of the voltage will be impacted, and all variations fall into the general category of power quality disturbances. Power quality can be improved if the disturbance signals are analyzed to take appropriate preventive measures. Voltage transient, sag, swell, and disruption signals are the power quality disturbance signals taken into consideration for analysis. Continuous wavelet transform, with Morlet wavelet as mother wavelet, is used for signal processing to extract usable information from the power quality disturbance signals. The wavelet toolbox in MATLAB is used to create the voltage signals and perform continuous wavelet analysis using both the command line interface and graphical interface. The continuous wavelet transform coefficients are displayed at their absolute values. The coefficients derived from continuous wavelet analysis can be used to quickly determine the type of disturbance resulting in visualization of the type of disturbance and corresponding information can be readily obtained from the coefficients obtained from continuous wavelet analysis.

The design and implementation of an automated cooling system for solar panels using an Arduino microcontroller, temperature sensor, voltage, relay, and Central Processing Unit (CPU) fan are detailed in this paper. The objective of the system is to enhance the efficiency of photo voltaic panels by maintaining their temperature within an optimal range. The system utilizes a temperature sensor and voltage sensor to continuously monitor the temperature of the photo voltaic panel. The Arduino microcontroller receives temperature readings from the sensor and based on the measured temperature, the microcontroller determines whether cooling is required. When the temperature exceeds a predefined threshold, the microcontroller triggers a motor driver module to switch ON the connected to a CPU fan and pump on. The motor driver controls the speed of the fan based on temperature. The fan, placed strategically near the photo voltaic panel, creates airflow that helps dissipate heat from the panel's surface. It periodically checks the temperature and controls the fan accordingly.

Voltage sag, swell, and interruption are the variations in supply voltage existing for a short duration. There is a necessity for an accurate analysis of voltage signals as the magnitude of supply voltage should be within an acceptance range. Wavelet transform, a signal processing technique, can be used to extract information about the disturbances in voltage signals. Short-duration voltage variations are decomposed using multiresolution analysis. The original voltage signals are decomposed into approximation and detail components at various levels by using discrete wavelet transform. Decomposition is carried up to seven levels and Daubechies 4 (db4) is used as prototype function. Different entropy criteria are applied for the coefficients of obtained approximation and detail components. The different entropy criteria include Shannon, log energy and norm entropies. One-dimensional wavelet decomposition analysis is carried out in MATLAB using the command line functions to present classification of short-duration voltage variations in a distribution system using entropy criteria.