Power quality is crucial for the reliable and efficient operation of domestic and industrial loads. Nonlinear power electronic converter loads have increased in usage, which has led to a decline in both voltage and current quality. To overcome these power quality issues, a universal power quality compensator (UPQC) has been developed and integrated into the power distribution network. The UPQC comprises dual active power filters which are equipped with a DC-link capacitor. The voltage of the DC-link capacitor is controlled by a DC-link voltage controller. However, the conventional PI controller is not suitable for regulating voltage at a defined level due to inappropriate gain values. In this work, we propose an intelligent adaptive neuro-fuzzy inference system-based DC voltage controller for customized UPQC devices. The proposed control scheme aims to mitigate the existing power quality challenges by reducing the issues in classical controllers and incorporating intelligence knowledge with subjective decisions. Using the MATLAB/Simulink software tool, we have tested a customized UPQC device controlled by ANFIS for critical operation and performance. The simulation results are presented along with valid comparisons.

A new optimization technique is proposed for solving optimization problems having single and multiple objectives, with objective functions such as generation cost, loss, and severity value. This algorithm was developed to satisfy the constraints, such as OPF constraints, and practical constraints, such as ram rate limits. Single and multi-objective optimization problems were implemented with the proposed hybrid fruit fly-based artificial bee colony (HFABC) algorithm and the non-dominated sorting hybrid fruit fly-based artificial bee colony (NSHFABC) algorithm. HFABC is a hybrid model of the fruit fly and ABC algorithms. Selecting the user choice-based solution from the Pareto set by the proposed NSHFABC algorithm is performed by a fuzzy decision-based mechanism. The proposed HFABC method for single-objective optimization was analyzed using the Himmelblau test function, Booth’s test function, and IEEE 30 and IEEE 118 bus standard test systems. The proposed NSHFABC method for multi-objective optimization was analyzed using Schaffer1, Schaffer2, and Kursawe test functions, and the IEEE 30 bus test system. The obtained results of the proposed methods were compared with the existing literature.

In this paper, a proposed Non-dominated Sorting Hybrid Cuckoo Search Algorithm (NSHCSA) for multi objective optimal power flow problem with FACTS devices namely Thyristor Controlled Series Compensator (TCSC) and Static Synchronous Series Compensator (SSSC) with different objective functions including the installation cost of FACTS devices are presented. The practical and operating constraints are considered for this analysis. The location of the FACTS device is selected to enhance the system security with respect to minimizing line overloads and bus voltage violations. The proposed Hybrid Cuckoo Search Algorithm (HCSA) is the combination of Cuckoo Search Algorithm (CSA) and Genetic Algorithm (GA). For multi objectives selected Pareto front is obtained by using the fuzzy decision making tool. The effectiveness of the proposed method is tested on standard IEEE-30 bus test system in the presence of the TCSC and SSSC. The results are analyzed and compared with existing methods.

Power Quality Analyzers (PQA) play an important role in monitoring and controlling health of the electrical systems. They can report the fluctuations in the field measurements with different power quality issues as well as due to load variations. Internet of Things (IoT) is a potential technology to design Smart PQAs for remote monitoring and easy integration of the field information on the cloud platform using gateway units. This paper focuses on the development of a Smart PQA system using low-cost IoT hardware and software design solutions. The hardware development is completed using Arduino Mega 2560 microcontroller in combination with ESP32 Wroom Wi-Fi gateway and SIM900A GSM gateway. The real-time field data is gathered at the ThingSpeak platform for future analysis, while GSM-based design ensures timely alerts to the end users for any major fluctuation in the power supply. The performance of the proposed low-cost system has been compared with the readings obtained from FPGA-based conventional PQA and standard Fluke Meter when connected to different loads. The proposed system output values are tabulated and compared using graphs. The proposed system is expected to be useful for the critical assessment, monitoring, and control of power quality parameters in various commercial and residential premises.