Wavelet Analysis- Singular Value Decomposition Based Method for Precise Fault Localization in Power Distribution Networks Using k-NN Classifier

In power systems, power quality issues raised by various non-linear devices (NLD) might violate the allowable harmonic limits defined by IEEE-519 standard. In order to maintain the total harmonic distortion well below the allowable limits, passive harmonic filters are commonly used. This paper presents an algorithm to optimally plan passive harmonic filters in the distribution networks to minimize the total voltage harmonic distortion. The optimization considers the filter location, size, type, quality factor and tuned harmonic order with constraints of bus voltage magnitudes, total and individual harmonic limits and filter components design and operating limits. The proposed approach is developed by using Particle Swarm Optimization (PSO) algorithm and tested on IEEE 18-bus and IEEE 33-bus radial distribution systems. The evaluation demonstrates the effectiveness of the optimization solution in identifying the best filter location, capacity and type.

This paper presents a comprehensive multi-objective optimization framework for enhancing distribution network flexibility through strategic integration of photovoltaic (PV) systems and battery energy storage systems (BESS). Using the IEEE 33-bus radial distribution system as a test case, a threestage comparative analysis is performed to evaluate grid performance improvements: (1) baseline performance, (2) operational optimization without physical distributed generation (DG), and (3) optimal PV-BESS placement using Particle Swarm Optimization (PSO). The methodology employs Particle Swarm Optimization (PSO) to minimize power losses while improving voltage profiles, power factor, reactive power losses and enhances grid flexibility through reactive power support and overall system power quality. Results demonstrate that coordinated PV-BESS integration reduces active power losses by $44.13 \%$ (from 200.28 kW to 111.89 kW) and mitigates IEEE 1547-2018 voltage violations, while constraining DG penetration to $30 \%$ of total load. The study confirms that while load optimization offers partial benefits, physical DG placement is essential for maximizing grid flexibility and power quality.

Power system flexibility is seen as a key for addressing the management challenges in the future smart grids. Distributed Energy Resources (DERs) can provide the necessary flexibility to: a) maintain a stable frequency and a secure energy supply in an overall system perspective, and b) maintain bus voltages and secure transfer capacities in their local networks. In this context, Flexibility Service Providers (FSPs) facilitate the management of the transmission and the distribution network, while at the same time they optimally exploit their DERs. This work proposes a bilevel model for an FSP owning distributed Battery Storage Units and participating in: i) wholesale Energy, Reserve and Balancing Markets, and ii) a novel distribution-level Flexibility Market. The developed model is applied to the IEEE 33-bus radial distribution system and the results demonstrate that it achieves superlinear gains. Finally, a sensitivity analysis is conducted to study the impact of several externalities on the FSP's decisions.

Several optimization techniques are applied to distribution system reconfiguration such as particle swarm optimization technique (PSO), genetic algorithm optimization (GAO) and grey wolf optimization technique (GWO). In this paper, grasshopper optimization algorithm is used to simulate distribution network reconfiguration to determine the optimal switches combination for maximizing power losses reduction taking into account the constraints of the system structure. The proposed technique mathematically simulates feeder reconfiguration inspired from behavior of grasshopper in nature, and then it is used to find the optimal configuration of network. The proposed method is tested on IEEE 33-bus radial distribution system and gave the optimal solution in a reasonable time of computation. The proposed simulation results proved reasonable computing time and high performance of the proposed method when compared with other optimization techniques.

Short circuit fault is one of the severe problems in the power system. This problem causes sag until it gets cleared. This paper proposes an optimization based on Genetic Algorithm (GA) to find the optimal placement and sizing of Dynamic Voltage Restorer (DVR) for voltage sag mitigation in the distribution system. Optimization is conducted with an objective function of minimizing the system losses. The proposed method is tested on the IEEE 33-bus radial distribution system using Matlab/Simulink. Simulation results show that different energy sources have different characteristics to respond voltage sag. Economical cost for investment on the type of energy storage for DVR is also being considered. The optimal result was obtained by combining both battery and supercapacitor as the source for DVR.

Passive distribution networks are being converted into active ones by incorporating distributed means of energy generation, consumption, and storage, and the formation of so-called microgrids (MGs). As the next generation of MGs, reconfigurable microgrids (RMGs) are still in early phase studies, and require further research. RMGs facilitate the integration of distributed generators (DGs) into distribution systems and enable a reconfigurable network topology by the help of remote-controlled switches (RCSs). This paper proposes a day-ahead operational scheduling framework for RMGs by simultaneously making an optimal reconfiguration plan and dispatching controllable distributed generation units (DGUs) considering power loss minimization as an objective. A hybrid approach combining conventional particle swarm optimization (PSO) and selective PSO (SPSO) methods (PSO&SPSO) is suggested for solving this combinatorial, non-linear, and NP-hard complex optimization problem. PSO-based methods are primarily considered here for our optimization problem, since they are efficient for power system optimization problems, easy to code, have a faster convergence rate, and have a substructure that is suitable for parallel calculation rather than other optimization methods. In order to evaluate the suggested method’s performance, it is applied to an IEEE 33-bus radial distribution system that is considered as an RMG. One-hour resolution of the simultaneous network reconfiguration (NR) and the optimal dispatch (OD) of distributed DGs are carried out prior to this main study in order to validate the effectiveness and superiority of the proposed approach by comparing relevant recent studies in the literature.

The proliferation of renewable generation into distribution networks requires a paradigm shift in designing traditional control and operation strategies regarding system resilience. Distribution networks can be viewed as a group of small microgrids (MGs) when individual MGs are in islanded operation. Therefore, to ensure stability in distribution networks, it is crucial to search for the optimal solutions to define the boundaries of these interconnected small MGs. This paper proposes a dynamic separation algorithm to enable a distribution network to respond to unforeseen disturbances effectively. To achieve this, we consider two constraints: maintaining a dynamic supply-demand balance and securing at least one reserve power source. The proposed algorithm utilizes K-means clustering algorithm based on partition matrix obtained from the $k$ -way partitioning scheme for the constraints. The proposed partition matrix can also minimize the total active power flows of distribution lines that connect different small microgrids. The performance of proposed algorithm was tested using a modified IEEE 33-bus radial distribution system and indicates that the proposed algorithm gains more advantages than the original k-way partitioning scheme has.

This paper proposes a novel integer-mixed probabilistic optimal power flow (IM-POPF) strategy for frequency regulation in islanded microgrids under uncertain operating conditions. Existing load-shedding approaches face critical limitations: continuous frameworks fail to reflect the discrete nature of actual load disconnections, while deterministic models inadequately capture the stochastic behavior of renewable generation and load variations. The proposed approach formulates load shedding as an integer optimization problem where variables are categorized as integer (load disconnection decisions at specific nodes) and continuous (voltages, power generation, and steady-state frequency), better reflecting practical power system operations. The key innovation combines integer load-shedding optimization with efficient uncertainty propagation through Unscented Transformation, eliminating the computational burden of Monte Carlo simulations while maintaining accuracy. Load and renewable uncertainties are modeled as normally distributed variables, and probabilistic constraints ensure operational limits compliance with predefined confidence levels. The methodology integrates Differential Evolution metaheuristics with Unscented Transformation for uncertainty propagation, requiring only 137 deterministic evaluations compared to 5000 for Monte Carlo methods. Validation on an IEEE 33-bus radial distribution system configured as an islanded microgrid demonstrates significant advantages over conventional approaches. Results show 36.5-fold computational efficiency improvement while achieving 95.28% confidence level compliance for frequency limits, compared to only 50% for deterministic methods. The integer formulation requires minimal additional load shedding (21.265%) compared to continuous approaches (20.682%), while better aligning with the discrete nature of real-world operational decisions. The proposed IM-POPF framework successfully minimizes total load shedding while maintaining frequency stability under uncertain conditions, providing a computationally efficient solution for real-time microgrid operation.

This paper presents a load shedding scheme for distribution systems based on recently developed revolutionary algorithm known as teaching learning based optimization (TLBO). To handle this optimization problem a multi-objective function that considers voltage stability margin and amount of load to be shed is formulated. The proposed load shedding scheme is evaluated through simulations on IEEE 33-bus radial distribution system with 2 distributed generation units using MATLAB® software. Furthermore, the effectiveness of the proposed method is verified by comparing obtained results with those achieved by using genetic algorithm (GA). The simulation results show that the proposed TLBO method is much more effective than GA.

This paper presents a comprehensive approach to improve the daily performance of an active distribution network (ADN), which includes renewable resources and responsive loads (RLs), using distribution network reconfiguration (DNR). The optimization objectives considered in this work can be described as (i) reducing active losses, (ii) improving the voltage profile, (iii) improving the network reliability, and (iv) minimizing the operation costs. The proposed approach also accounts for the probability of renewable resource failure given the information collected from their initial state at the beginning of each day. Furthermore, solar radiation variations are estimated based on past historical data, and the impact of the performance of renewable resources such as photovoltaics (PVs) is determined hourly based on a Markov model. Since the number of reconfiguration scenarios is very large, stochastic DNR (SDNR) based on the probability distance method is employed to shrink the scenarios set, before a self-adaptive modified crow search algorithm (SAMCSA) is introduced to find an optimal scenario. Finally, the IEEE 33-bus radial distribution system and the 86-bus Taiwan Power Company (TPC) system are investigated as two case studies to verify the effectiveness of the proposed method.

There is increasing growth in load demands and financial strain to upgrade the present power distribution system. It faces challenges such as power losses, voltage deviations, lack of reliability and voltage instability. There is also a sense of responsibility in the wake of environmental and energy crises to adopt distributed renewable resources for power generation. These challenges can be resolved by optimally allocating distributed generators (DGs) at different suitable locations in the radial power distribution system. Optimal allocation is a non-linear problem which is solved by powerful metaheuristic optimization algorithms. In this work, an objective function is introduced to optimally size four different types of DGs by utilizing honey badger algorithm (HBA), and comparison is drawn with grey wolf optimization (GWO) and whale optimization algorithm (WOA). The objective is to boost the voltage profile and minimize the power losses of the standard IEEE 33bus and 69-bus radial power distribution system. It is observed from the simulation results that honey badger algorithm is faster than grey wolf optimization and whale optimization algorithm in reaching accurate and optimum results in a mere one and two iterations for IEEE 33-bus and 69-bus systems, respectively. Additionally, power losses are reduced to 71% and 70% for IEEE 33-bus and 69-bus, respectively.

A new meta-heuristic method, comprehensive teaching learning harmony search optimization algorithm (CTLHSO), is developed in this paper for the simultaneous reconfiguration and optimal allocation of distributed generation resources in radial distribution systems. The proposed method is a hybridization of the teaching–learning-based optimization (TLBO) and the harmony search (HS) algorithms. Primarily, eleven mathematical benchmark functions are used to test the performance of the CTLHSO algorithm. The results are then compared with that of global best artificial bee colony (G-ABC), particle swarm artificial bee colony (PS-ABC), TLBO and improved TLBO (I-TLBO) with identical parameters and initial conditions. The results show that the CTLHSO performance is better than the G-ABC, PS-ABC, TLBO and I-TLBO. Subsequently, CTLHSO is implemented on the IEEE 33-bus and 69-bus radial distribution systems for network reconfiguration and optimal placement of distributed generation resources to minimize the power losses and improve the voltage profiles. Five case studies at three different load levels are carried out. The results obtained are found to be better than those obtained with HS algorithm, genetic algorithm, refined genetic algorithm and fireworks algorithm.

Cloud theory-based multi-objective feeder reconfiguration problem considering wind power uncertainty

The demand of electricity is increasing very rapidly, with this increase in demand the burden on power system is increasing to meet this increasing demand, but it is very challenging because, the power system which we are using is aradial bus distribution system. The main problem while using redial system as a distribution network is the voltage drop which distorts the voltage profile of whole network. The primary cause of reduction of system voltage is, a huge amount of reactive power requirement of the load, because a large part of load is inductive. Around 13% of the generated power is wasted during transmission of power, so it is very necessary to improve voltage profile and to increase the efficiency of the network to prevent this loss of power. The PSO approach has been implemented to improve the voltage profile by optimizing the location of capacitor and to find its size. This test has been performed on the IEEE 33-bus and 66-bus radial distribution system which is designed with the use of MATLAB Software. The maximum number of capacitor units appropriate for the improvement for voltage profile is also checked in this work.

In coupled power distribution and transportation (CPT) system, a joint scheduling framework for mobile energy storage systems (MESSs) and Volt/VAR control (VVC) ensures reliable power distribution grid operations while supporting electric vehicle loads at electric vehicle charging stations (EVCSs). However, conventional model-based optimization methods for MESS scheduling and VVC may yield suboptimal solutions and greater computation times because of MESS operation and VVC in uncertain environment of CPT systems. To resolve this issue, this study proposes a model-free deep reinforcement learning (DRL) framework. In this framework, smart inverters of MESSs and solar photovoltaic (PV) systems cooperate to minimize the real power loss and mitigate the violations of both MESSs’ state of charge (SOC) and voltage in the power distribution network, while MESSs travel via the transportation network to satisfy EV loads at EVCSs. A MESS routing algorithm based on Dijkstra’s algorithm is developed to determine the optimal destinations of the MESSs. In addition, two safety modules are developed to ensure that neither SOC nor voltage violations occur by adjusting real and/or reactive power of MESSs and PV systems during the training process. The developed MESS routing algorithm and safety modules are integrated into the proposed DRL framework, wherein the DRL agent performs the desired MESS scheduling and VVC through safe exploration during the training procedure. The proposed approach is tested in coupled IEEE 33-bus power distribution and 15-node transportation systems and coupled IEEE 57-bus power distribution and 42-node transportation systems. Numerical examples demonstrate the advantages of the proposed approach in terms of training convergence, real power loss, and SOC/voltage violation.