Three-phase Distribution System Research Articles

Optimal placement of uPMUs to improve the reliability of distribution systems through genetic algorithm and variable neighborhood search

Due to the dynamic nature of modern distribution systems, the deployment of micro-phasor measurement units (uPMU) is becoming increasingly common among utilities to improve system monitoring and reliability. However, given their high investment costs, deploying a large number of these devices becomes unfeasible. Hence, unlike other approaches found in the literature that focus on observability criteria, this work presents an algorithm for optimal placement of uPMUs aimed at improving distribution system reliability. The algorithm defines the optimal number and location of the uPMUs through an objective function based on the resolution of a fault location technique that works in conjunction with pseudo-measurements to successfully locate a contingency. The meta-heuristics Genetic Algorithm and Reduced Variable Neighborhood Search are employed to address this problem. The proposed method has been validated on a three-phase 39-bus distribution system and a real distribution feeder with 962 buses from an Ecuadorian electric distribution utility. The results obtained confirm the effectiveness of the method, as with the deployment of only two uPMUs, the energy not supplied decreases by 13.84 % and 24.96 % for the 39-bus and 962-bus systems, respectively. Moreover, in the 962-bus system, the System Average Interruption Duration Index (SAIDI) is reduced by 20.36 %.

Performance evaluation of solar-PV integrated hybrid fuzzy-logic controlled multi-functional UPQC for enhancing PQ features

To improve distribution system voltage and current quality, a newly built solar-PV system connected multi-functional universal power quality compensator (MFUPQC) has been extensively used. The proposed MFUPQC mitigates both load and source-side concerns in a three-phase distribution system. Furthermore, as part of the distributed generation scheme, active power from solar PV is injected into the grid or source when solar PV is available. In this context, the proposed MFUPQC was tested in both PQ enhancement and DG integration modes using a feasible control scheme. The proportional-integral controller is used for shunt- voltage-source inverter (VSI) DC-link control, which is not suitable for regulating DC-link voltage at the desired level due to incorrect gain value selection. In this work, an intelligent hybrid-fuzzy-logic DC-link control of MFUPQC evidences the intelligent knowledge base for better regulation of power-quality issues. The suggested hybrid fuzzy-logic controlled MFUPQC device's performance for both power quality (PQ) improvement and DG integration is validated using the MATLAB/Simulink software tool, and simulation results are provided with an appealing comparison analysis.

CHBMLI based DSTATCOM for power quality improvemt in a three-phase three-wire distribution system with PI controller

This paper presents a cascaded h-bridge (CHB) multilevel inverter (MLI) based MLI based distribution static synchronous compensator (DSTATCOM). The main objective of this paper is to reduce source current harmonics in the distribution system by using a cascade H-bridge multilevel inverter (CHBMLI) as DSTATCOM with a proportional-integral (PI) controller. The PI controller compensates reactive power, reduces source current harmonics, and maintains the unity power factor in the distribution system. The conventional two-level inverter-based DSTATCOM has many disadvantages such as high total harmonic distortion (THD), and high switching stress power semi-conductor devices, suitable for only low-power applications. This paper to overcome these drawbacks by using MLI-based DSTATCOM. The proposed system simulations are verified in MATLAB/Simulink software. The verified results are source current, load current, and compensating current.

Pelican-FOPID Based DSTATCOM for real-time load compensation and harmonics mitigation in three-phase distribution system

Power quality (PQ) explores the issues resulting from current and voltage deviations. Non-sinusoidal currents were drawn from the electric grid by the nonlinear loads. These non-sinusoidal currents contain harmonics and reactive power that lower the system's overall power quality. Distribution Static Compensator (DSTATCOM) emerged as a promising compensator to provide solution for all PQ issues. Yet the controlling strategy of this compensator was still complex to design. Here, an optimization-based Fractional Order Proportional Integral Derivative (FOPID) was introduced to manage the DSTATCOM process to improve PQ in a standard bus system. The optimal problem of FOPID controller was solved through the use of novel optimization approach. The DSTATCOM with FOPID was linked to the weak bus system. The pulse-signal of DSTATCOM was done using the optimal controller, which analyzes the error value of reference voltage and bus voltage to generate-pulses. The proposed model was designed, and the performance was analyzed under two cases, like IEEE 33 bus system and IEEE 13 bus system. The proposed optimal controller-based DSTATCOM performance was validated under various conditions such as interruption, swell, harmonics and sag. The optimal controller offered Total Harmonic Distortion (THD) value at sag, swell, and interruption period was 0.46%, 1.36% and 0.46% in IEEE 13 bus system, and 1.79%, 2.10% and 2.67% THD in IEEE 33 bus system. Moreover, the issues mitigation performance was compared to another present approach. The validated outcome demonstrates the proposed model provides a good mitigation performance in all PQ issues conditions, so it was well fit for real-time implementation.

Power Quality Improvement on 11kV/440V Distribution System using DSTATCOM

At the PCC and source side, current and voltage harmonics are compensated by utilizing a distribution static compensator. In this work, we also compensated for voltage control and reactive power correction. An improved reference current switching signal is suggested using a synchronous reference frame control technique. The analysis is done on an 11kV/440V three-phase four-wire unbalanced nonlinear load distribution system. The DC link voltage and harmonic reduction of the suggested distribution system performance is compared with the PI and fuzzy logic controllers. Harmonic distortion is reduced, and reactive power is successfully compensated with the suggested method. These simulation results are obtained by MATLAB/SIMULINK software.

Tractable Data Enriched Distributionally Robust Chance-Constrained Conservation Voltage Reduction

This paper proposes a tractable distributionally robust chance-constrained conservation voltage reduction (DRCC-CVR) method with enriched data-based ambiguity set in unbalanced three-phase distribution systems. The increasing penetration of distributed renewable generation not only brings clean power but also challenges the voltage regulation and energy-saving performance of CVR by introducing high uncertainties to distribution systems. In most cases, the conventional robust optimization methods for CVR only provide conservative solutions. To better consider the impacts of load and PV generation uncertainties on CVR implementation in distribution systems and provide less conservative solutions, this paper develops a data-based DRCC-CVR model with tractable reformulation and data enrichment method. Even though the uncertainties of load and photovoltaic (PV) can be captured by data, the availability of smart meters (SMs) and micro-phasor measurement units (PMUs) is restricted by cost budget. The limited data access may hinder the performance of the proposed DRCC-CVR. Thus, we further present a data enrichment method to statistically recover the high-resolution load and PV generation data from low-resolution data with Gaussian Process Regression (GPR) and Markov Chain (MC) models, which can be used to construct a data-based moment ambiguity set of uncertainty distributions for the proposed DRCC-CVR. Finally, the nonlinear power flow and voltage dependant load models and DRCC with moment-based ambiguity set are reformulated to be computationally tractable and tested on a real distribution feeder in Midwest U. S. to validate the effectiveness and robustness of the proposed method.

A Dual Fundamental Current Extraction Controller for DSTATCOM in the Distribution System for Power Quality Improvement Under Non-Ideal Source Voltage Condition

Most of the controllers often fail to work properly when encountered with power quality (PQ) issues like unbalanced and distorted grid voltage conditions in three-phase distribution system (TPDS). To address this problem, usually a dynamic static compensator (DSTATCOM) is utilized in TPDS. For enhanced performance, a novel dual fundamental component extraction (DFCE) technique is proposed for generating reference currents (RC) in a TPDS exhausting DSTATCOM. The proposed controller is highly effective for reactive power compensation and source current harmonic suppression under source unbalanced voltages condition with nonlinear load conditions. For computation of RC and phase synchronization, the controller extracts the required fundamental voltage and current at the same time. Consequently, the controller is able to control DSTATCOM operation in TPDS without any phase locked loop (PLL) and low pass filter (LPF). For generation of fundamental components of voltage and current, projected controller utilizes the self-tuning filter (STF). The efficacy of the controller is established in comparison to existing controllers like synchronous reference frame theory, p-q theory and STF-p-q under different grid voltage conditions. To enhance PQ, the suggested controller works efficiently for fundamental current component extraction without using PLL and low/high pass filter in case of grid distorted voltage condition.

A Novel ZSV-Based Detection Scheme for FDIAs in Multiphase Power Distribution Systems

Modern power systems are vulnerable to false data injection attacks (FDIAs) due to the widespread applications of two-way communication networks for system operation and control. Diagnosing such malicious attacks are of great significance for the resilient operations of power systems. Yet, in literature, the majority of studies in FDIA detection focus on power transmission systems. In this paper, a novel FDIA detection scheme is proposed for three-phase distribution systems based on zero-sequence voltage (ZSV). From the voltage and power measurements, the bus voltages are estimated, and then the estimated ZSV is calculated as the sum of the estimated bus voltages on the three phases to represent the degree of unbalance of the distribution system. Via mathematical analysis of the linear distribution system state estimation (DSSE) model, the distribution of the estimated ZSV under the normal condition is derived, based on which a whitening process is adopted on the estimated ZSV to weaken the effect of measurement noises. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {L}_{2}$ </tex-math></inline-formula> -norm of the whitened ZSV vector is then compared with a predefined threshold for FDIA detection. Moreover, the probability of false alarm of the proposed scheme is derived, which can be utilized to determine the detection threshold for a desired tolerance of false alarm rate. The proposed scheme is validated on several IEEE Test Feeders and simulation results show the effectiveness of the proposed scheme in detecting FDIAs in three-phase distribution systems.

Combined DC-Link Fed Parallel-VSI-Based DSTATCOM for Power Quality Improvement of a Solar DG Integrated System

In present day power systems, Power Quality (PQ) issues are causing great concern owing to the increased use of power electronic controlled drives and fluctuating and other non-linear loads. This problem is further aggravated by a steady increase in the integration of renewable energy-based Distribution Generation (DG), employing power electronic converters to distribution systems. Custom power devices with suitable control strategies provide an effective solution to these power quality issues. In this work, a typical three-phase distribution system supplying non-linear load and with DG integration is considered. A shunt connected DSTATCOM at PCC of the system is employed to mitigate power quality concerns. Initially, a parallel-VSI based DSTATCOM configuration, employing individual DC-Link and working basically on the principle of current sharing, has been proposed. The analysis is carried out for variable load conditions for PQ enhancement making use of a more effective control theory viz. Instantaneous Real-Reactive Power (IRP) theory for the generation of suitable switching patterns to the individual VSIs of the parallel DSTATCOM. Further, an improvement over the above configuration viz. combined/common DC-Link-fed parallel DSTATCOM is proposed. This configuration has the advantages of minimized sensing elements, reliable operation and low-cost compensation. A similar analysis is carried out for PQ improvement, making use of the same IRP theory with some modifications (known as MIRP theory). The effectiveness of this configuration is established from the simulation results. In all the above cases, the analyses are carried out using MATLAB/Simulink platform and the simulation results are presented in detail. Thus, the proposed parallel VSIs-based DSTATCOM configurations employing suitable control strategies provide effective solutions for power quality issues under varying load conditions in conventional distribution systems.

A Hybrid Compensator for Unbalanced AC Distribution System With Renewable Power

A hybrid compensator with a new coordinated control is proposed for mitigating power quality aspects and integration of renewable power in an unbalanced three-phase four-wire distribution system. The proposed compensator comprises of thyristor switched capacitor (TSC) banks, delta connected thyristor controlled reactor (TCR), a 3rd and a 5th harmonic tuned passive LC filter & a low rating three-phase four-wire D-STATCOM. The TSC-TCR operates asymmetrically for the base var compensation and also eliminates negative sequence current during unbalanced loading at the point of common coupling (PCC). The D-STATCOM works in coordination with the TSC-TCR and the passive filters to compensate fundamental var, zero sequence current, voltage flicker & 7th harmonic currents at the PCC. The D-STATCOM thus serves as a supportive var compensator as well as an active filter in selective harmonic compensation mode, thereby improving the var compensation and harmonic current filtering at the PCC. A new coordination control system is proposed to control the operation of the D-STATCOM which is also employed to integrate a PV source & a doubly fed induction generator (DFIG) based wind energy conversion system (WECS) to the PCC. This scheme with grid connected and islanded mode of operation using D-STATCOM exhibits low-voltage ride-through capabilities. Further, the chance of resonance of the passive filters used with source inductance is eliminated in the proposed scheme. Simulation results show that the proposed hybrid system effectively compensates var, negative & zero sequence current, reduces voltage flicker and harmonic currents at PCC.

Log(v) 3LPF: A Linear Power Flow Formulation for Unbalanced Three-Phase Distribution Systems

In this work, we introduce Log(v) 3LPF, a linear power flow solver for unbalanced three-phase distribution systems. Log(v) 3LPF uses a logarithmic transform of the voltage phasor to linearize the AC power flow equations around the balanced case. We incorporate the modeling of ZIP loads, transformers, capacitor banks, switches and their corresponding controls and express the network equations in matrix-vector form. With scalability in mind, special attention is given to the computation of the inverse of the system admittance matrix, Ybus. We use the Sherman-Morrison-Woodbury identity for an efficient computation of the inverse of a rank-k corrected matrix and compare the performance of this method with traditional LU decomposition methods using Big- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {O}$</tex-math></inline-formula> notation. We showcase the solver for a variety of network sizes, ranging from tens to thousands of nodes, and compare the Log(v) 3LPF with commercial-grade software, such as OpenDSS.

Multi-Stage Volt/VAR Support in Distribution Grids: Risk-Aware Scheduling With Real-Time Reinforcement Learning Control

The ever-increasing penetration of intermittent renewable resources in low-voltage power grids necessitates efficient operational strategies for voltage regulation as well as power scheduling of the available resources. In this paper, a risk-aware Volt/VAR support framework followed by a real-time reinforcement learning controller is presented for three-phase distribution systems. In the risk-aware stochastic scheduling stage, the legacy voltage regulating assets along with inverter-based photovoltaics (PVs) and energy storage system (ESS) are optimized considering day-ahead and intra-day markets. Moreover, demand response (DR) and voltage reduction plans are included in the proposed scheduling framework. By incorporating voltage-dependent load modeling in this study, the implementation of the voltage reduction plan reduces energy consumption in feeders by running the network at lower permissible voltage limits. The shiftable loads under the DR program are employed for peak shaving and to reduce operational costs. The result shows that DR implementation also reduces dependencies on the operation of traditional devices. The stochasticity of abrupt changes in PV generations is represented as the Gaussian Mixture Model (GMM), indicating a non-unimodal probability distribution in day-ahead PV forecasting errors. The scenario sets for uncertain variables are then reduced using a fuzzy clustering technique. Decisions made in the scheduling, associated with PV inverters and ESS operation, are revised with a real-time controller, i.e., Deep Deterministic Policy Gradient (DDPG) reinforcement learning. The DDPG is adopted in the control stage of the framework considering the detailed modeling of unbalanced three-phase distribution grids to minimize the voltage deviation and power ramping of ESS. The performance of the proposed multi-stage scheme is verified using a three-phase active distribution grid under different scenarios.

Improving Power Quality by DSTATCOM Based DQ Theory with Soft Computing Techniques

The development of non-linear loads at consumers has significantly impacted power supply systems. Since, the poor power quality has been found in the three-phase distribution system due to unbalanced loads, harmonic current, undesired voltage regulation, and extreme reactive power demand. To overcome this issue, Distributed STATicCOMpensator (DSTATCOM) is implemented. DSTATCOM is a shunt-connected Voltage Source Converter (VSC) that has been utilized in distribution networks to balance the bus voltage in terms of enhancing reactive power control and power factor. DSTATCOM can provide both rapid and continuous capacitive and inductive mode compensation. A rectified resistive and inductive load eliminates current harmonics in a three-phase power supply. The synchronous fundamental DQ frame is a time-domain approach developed from three-phase system space vector transformations has been designed using MATLAB/Simulink. The DQ theory is used to produce the reference signal for the Pulse Width Modulation (PWM) generator. In addition, a traditional Proportional Integral Derivative (PID) controller is designed and compared with proposed soft computing approaches such as Fuzzy–PID and Artificial Neural Network (ANN-PID) and compared accurate reference current determination for Direct Current (DC) bus through DC link. An Analytical explores the proposed control strategies given to establish superior outcomes. Finally, total harmonic distortion analysis should be taken for performance analysis of the proposed system with IEEE standards.

Expansion planning of joint medium- and low-voltage three-phase distribution networks considering the optimal integration of distributed energy resources

This article proposes a novel hybrid approach based on the three-phase bus impedance matrix to solve the medium and low voltage planning problem of three-phase distribution systems considering the optimal integration of battery energy storage systems (BESSs) and different renewable energy sources (RESs). The mathematical formulation to describe the problem involves a mixed-integer non-linear model that considers the allocation and selection problem of distribution substations and transformers, medium and low voltage branches, RESs, and BESSs. Furthermore, the optimal operation of BESSs, the load balancing among low voltage system phases, the upgrading of existing elements, and the uncertainty of RESs, demands, and energy prices are considered. To solve the investment problem, an Iterated Local Search algorithm with neighborhood structures is applied, which uses the electrical information from the three-phase bus impedance matrix to generate new solutions. In addition, a two-step decomposition stochastic optimal power flow is proposed to solve the operational problem considering the stochastic scenarios. The performance of the new methodology is tested on a real distribution system. The numerical results show that integrating RESs and BESSs in the planning problem minimizes the cost of energy losses by 14.16% and the energy purchased from substations by 18.78%.

State Estimation of Asymmetrical Distribution Networks by μ-PMU Allocation: A Novel Stochastic Two-stage Programming

• The WLS-based state estimation algorithm developed in this research can simulate large-scale asymmetric distribution networks. • An effective DSSE model is implemented based on a limited number of μ-PMUs and ZINs for determining the state variables. • Using the ZIN characteristics to minimize the μ-PMUs number in the system and keep the system fully observable. • The linear approximations are applied to model the linear DSSE. Distribution system state estimation provides essential data for the system monitoring and control, which some uncertain parameters such as the intermittent and varying output of distributed generation (DG), random meter errors, and inaccurate network parameters make situational awareness (SA) of distribution systems a challenging issue. To address these issues, this paper develops an innovative two-stage stochastic programming model, where in the first stage, optimal μ-PMU placement is implemented aiming at minimizing the installation cost of μ-PMU and maximizing the number of measurement redundancy and the system observability in the presence of partially zero injection nodes (PZIN), while in the second stage state estimation of three-phase asymmetric DG-integrated distribution systems is performed to enhance SA. By applying the proposed model, the optimal locations of μ-PMUs in the presence of PZINs and various contingencies were achieved, and the distribution system state estimation was obtained with high accuracy and low error percentage. The feasibility of the proposed methodology is verified on the modified IEEE 85-bus distribution system.

Cone-structured seven-level boost inverter topology for improvising power quality using online monitoring controller scheme for DSTATCOM application

In this article, a seven-level triple-time voltage-boosting topology (8S7L-TTB) is proposed, which has eight switches as the minimum. This topology is used as DSTATCOM to eliminate power-quality issues. In a three-phase four-wire distribution system, specific unpredictable issues have emerged through the increase of unbalanced linear and non-linear loads, which has caused various drawbacks such as voltage imbalance, poor voltage regulation, increased reactive components, and harmonics generation, through which the life period of the system has been minimized, undesirable heating has been produced, and the RMS voltage has been reduced. Hence, it is highly essential to eliminate the issues as mentioned earlier. In this research, a PV-based DSTATCOM is introduced for online-monitoring adaptive Chebyshev neural network controller with triple-boost inverter topology. The three-phase system’s D-Q components, which have been continuously extracted under instant-loading conditions, and the reference magnitude, have been compared through the proposed controller. The error signal is received from the adaptive Chebyshev neural network controller, through which the proposed inverter-switching devices are triggered with multi-carrier pulse width-modulation technique are used to compute the THD of 2.79%. Furthermore, this proposed seven-level inverter and controller have solved the aforementioned problems and maintained the floating capacitor’s voltage nearly as similar as the source voltage in different loading conditions to ensure the efficiency of the system is at 96.82%. To ensure its suitability in real-time, the proposed controller is simulated with MATLAB software and validated through the downscale experimental setup and the results are observed.

Application of Deep Learning Gated Recurrent Unit in Hybrid Shunt Active Power Filter for Power Quality Enhancement

This research work aims at providing power quality improvement for the nonlinear load to improve the system performance indices by eliminating maximum total harmonic distortion (THD) and reducing neutral wire current. The idea is to integrate a shunt hybrid active power filter (SHAPF) with the system using machine learning control techniques. The system proposed has been evaluated under an artificial neural network (ANN), gated recurrent unit, and long short-term memory for the optimization of the SHAPF. The method is based on the detection of harmonic presence in the power system by testing and comparison of traditional pq0 theory and deep learning neural networks. The results obtained through the proposed methodology meet all the suggested international standards of THD. The results also satisfy the current removal from the neutral wire and deal efficiently with minor DC voltage variations occurring in the voltage-regulating current. The proposed algorithms have been evaluated on the performance indices of accuracy and computational complexities, which show effective results in terms of 99% accuracy and computational complexities. deep learning-based findings are compared based on their root-mean-square error (RMSE) and loss function. The proposed system can be applied for domestic and industrial load conditions in a four-wire three-phase power distribution system for harmonic mitigation.

Optimal Selection of Conductor Sizes in Three-Phase Asymmetric Distribution Networks Considering Optimal Phase-Balancing: An Application of the Salp Swarm Algorithm

This paper presents a new methodology to simultaneously solve the optimal conductor selection and optimal phase-balancing problems in unbalanced three-phase distribution systems. Both problems were represented by means of a mathematical model known as the Mixed-Integer Nonlinear Programming (MINLP) model, and the objective function was the minimization of the total annual operating costs. The latter included the costs associated with energy losses, investment in conductors per network segment, and phase reconfiguration at each node in the system. To solve the problem addressed in this study, a master–slave methodology was implemented. The master stage employs a discrete version of the Salp Swarm Algorithm (SSA) to determine the set of conductors to be installed in each line, as well as the set of connections per phase at each of the nodes that compose the system. Afterward, the slave stage uses the three-phase version of the backward/forward sweep power flow method to determine the value of the fitness function of each individual provided by the master stage. Compared to those of the Hurricane-based Optimization Algorithm (HOA) and the Sine Cosine Algorithm (SCA), the numerical results obtained by the proposed solution methodology in the IEEE 8- and 25-node test systems demonstrate its applicability and effectiveness. All the numerical validations were performed in MATLAB.

Estimate Three-Phase Distribution Line Parameters With Physics-Informed Graphical Learning Method

Accurate estimates of network parameters are essential for modeling, monitoring, and control in power distribution systems. In this paper, we develop a physics-informed graphical learning algorithm to estimate network parameters of three-phase power distribution systems. Our proposed algorithm uses only readily available smart meter data to estimate the three-phase series resistance and reactance of the primary distribution line segments. We first develop a parametric physics-based model to replace the black-box deep neural networks in the conventional graphical neural network (GNN). Then we derive the gradient of the loss function with respect to the network parameters and use stochastic gradient descent (SGD) to estimate the physical parameters. Prior knowledge of network parameters is also considered to further improve the accuracy of estimation. Comprehensive numerical study results show that our proposed algorithm yields high accuracy and outperforms existing methods.

Adjustable robust approach to increase DG hosting capacity in active distribution systems

We propose a robust coordinated dynamic hosting capacity (HC) evaluation method considering active network management (ANM) techniques in three-phase distribution systems. Our proposed method extends the literature in two main ways. Firstly, by applying the adjustable robust counterpart methodology, we allow fast-acting devices, such as inverters, to take live recourse actions in response to demand and generation uncertainties while not making the unrealistic assumption that they have real-time communication with a central entity. Secondly, we propose a linear uncertainty model capable of handling the strong spatial correlation between PV outputs. We show that our proposed linear model, compared to the existing linearised ellipsoidal uncertainty model, significantly reduces the computational burden while introducing a minor error. We validate the optimality and robustness of our proposed methods via numerical tests on the IEEE 37-node unbalanced three-phase distribution system.