Delay-Aware Semantic Sampling in Power Electronic Systems

In power electronic systems (PES), attacks on data availability such as latency attacks, data dropouts, and time-synchronization attacks (TSAs) continue to pose significant threats to both the communication network and the control system performance. As per the conventional norms of communication engineering, PES still rely on time synchronized sampling, which translates every received message with equal importance. In this paper, we go beyond event-triggered sampling/estimation to integrate semantic principles into the sampling process for each distributed energy resource (DER), which not only compensates for delayed communicated signals by reconstruction of a new signal from the inner control layer dynamics, but also evaluates the reconstruction stage using key semantic requirements, namely Freshness, Relevance and Priority for good dynamic performance. As a result, the sparsity provided by event-driven sampling of internal control loop dynamics translates as semantics in PES. The proposed scheme has been extensively tested and validated on a modified IEEE 37-bus AC distribution system, under many operating conditions and noisy environment in OPAL-RT environment to establish its robustness, model-free design ability and adaptive behavior to dynamic cyber graph topologies

Model-Agnostic Semantics in Shipboard Systems Against Data Availability Attacks

Data availability attacks, such as latency attacks, data dropouts and time synchronization attacks (TSAs) still remain a prime concern not only at the network level but also impair the control system performance & stability of the physical layer in medium voltage DC (MVDC) shipboard power systems (SPSs). To address its impact on the physical layer, we equip a model-agnostic semantic architecture that can compensate using process-aware delay compensation capabilities. Unlike traditional model predictive controllers (MPCs) with a limited prediction horizon, the proposed architecture offers long event-driven prediction even during large random delayed measurements. The semantic prediction policy is governed using the inner control loop dynamics of power generation modules (PGMs) to provide reconstructed signals for delay compensation. Its robustness has been extensively tested and validated on nominal 12 kV two-zone MVDC SPS, in an OPAL-RT environment for above-mentioned attacks. Overall, the proposed model-agnostic estimator (MAE) has the potential to significantly improve the resilience of SPSs against cyber-attacks without revealing any model information

A framework of L-HC and AM-MKF for accurate harmonic supportive control schemes

In this paper, an enhanced optimal control technique based on adaptive Maximize-M Kalman filter (AM-MKF) is used. To maximize power extraction from solar PV (Photovoltaic) panel, a learning-based hill climbing (L-HC) algorithm is implemented for a grid integrated solar PV system. For the testing, a three-phase system configuration based on 2-stage topology, and the deployed load on a common connection point (CCP) are considered. The L-HC MPPT algorithm is the modified version of HC (Hill Climbing) algorithm, where issues like, oscillation in steady-state condition and, slow response during dynamic change condition are mitigated. The AM-MKF is an advanced version of KF (Kalman Filter), where for optimal estimation in KF, an AM-M (Adaptive Maximize-M) concept is integrated. The key objective of the novel control strategy is to extract maximum power from the solar panel and to meet the demand of the load. After satisfying the load demand, the rest power is transferred to the grid. However, in the nighttime, the system is used for reactive power support, which mode of operation is known as a DSTATCOM (Distribution Static Compensator). The capability of developed control strategies, is proven through testing on a prototype. During experimentation, different adverse grid conditions, unbalanced load situation and variable solar insolation are considered. In these situations, the satisfactory performances of control techniques prove the effectiveness of the developed control strategy

On the assessment of cyber risks and attack surfaces in a real-time co-simulation cybersecurity testbed for inverter-based microgrids

The integration of variable distributed generations (DGs) and loads in microgrids (MGs) has made the reliance on communication systems inevitable for information exchange in both control and protection architectures to enhance the overall system reliability, resiliency and sustainability. This communication backbone in turn also exposes MGs to potential malicious cyber attacks. To study these vulnerabilities and impacts of various cyber attacks, testbeds play a crucial role in managing their complexity. This research work presents a detailed study of the development of a real-time co-simulation testbed for inverter-based MGs. It consists of a OP5700 real-time simulator, which is used to emulate both the physical and cyber layer of an AC MG in real time through HYPERSIM software; and SEL-3530 Real-Time Automation Controller (RTAC) hardware configured with ACSELERATOR RTAC SEL-5033 software. A human–machine interface (HMI) is used for local/remote monitoring and control. The creation and management of HMI is carried out in ACSELERATOR Diagram Builder SEL-5035 software. Furthermore, communication protocols such as Modbus, sampled measured values (SMVs), generic object-oriented substation event (GOOSE) and distributed network protocol 3 (DNP3) on an Ethernet-based interface were established, which map the interaction among the corresponding nodes of cyber-physical layers and also synchronizes data transmission between the systems. The testbed not only provides a real-time co-simulation environment for the validation of the control and protection algorithms but also extends to the verification of various detection and mitigation algorithms. Moreover, an attack scenario is also presented to demonstrate the ability of the testbed. Finally, challenges and future research directions are recognized and discussed

Leaky least logarithmic absolute difference based control algorithm and learning based InC MPPT technique for grid integrated PV system

This paper introduces a novel leaky least logarithmic absolute difference (LLLAD)-based control algorithm and a learning-based incremental conductance (LIC) maximum power point tracking algorithm for a grid-integrated solar photovoltaic (PV) system. Here, a three-phase topology of the grid-integrated PV system is implemented, with the nonlinear/linear loads. The proposed LIC technique is an improved form of an incremental conductance (InC) algorithm, where inherent problems of the traditional InC technique, such as steady-state oscillations, slow dynamic responses, and fixed-step-size issues, are successfully mitigated. The prime objective of the proposed LLLAD control is to meet the active power requirement of the loads from the generated solar PV power, and after satisfying the load demand, the excess power is supplied to the grid. However, when the generated solar power is less than the load demand, then LLLAD meets the load by taking extra required power from the grid. During these power management processes, on the grid side, the power quality is maintained. During daytime, the proposed control technique provides load balancing, power factor correction, and harmonic filtering. Moreover, when solar irradiation is zero, then the dc-link capacitor and a voltage-source converter act as a distribution static compensator, which enhances the utilization factor of the system. The proposed techniques are modeled, and their performances are verified experimentally on a developed prototype in solar insolation variation conditions, unbalanced loading, and in different grid disturbances such as over- and undervoltage, phase imbalance, harmonics distortion in the grid voltage, etc. Test results have met the objectives of the proposed paper, and parameters are under the permissible limit, according to the IEEE-519 standard

A Monolithic Cybersecurity Architecture for Power Electronic Systems

Power electronic systems (PES) face significant threats from various data availability and integrity attacks, significantly affecting the performance of communication networks and power system operation. As a result, several attack detection and reconstruction techniques are deployed, which makes it a costly & complex cybersecurity operational platform with significant room for incremental extensions for mitigation against future threats. Unlike the said traditional arrangements, our paper introduces a foundational approach by establishing a monolithic cybersecurity architecture (MCA) via incorporating semantic principles into the sampling process for distributed energy resources (DERs). This unified approach concurrently compensates for the intrusion challenges posed by cyber attacks by reconstructing signals using the dynamics of the inner control layer. This reconstruction considers essential semantic attributes, like Priority, Freshness, and Relevance to ensure resilient dynamic performance. Hence, the proposed scheme promises a generalized route to concurrently tackle a global set of cyber attacks in elevating the resilience of PES. Finally, rigorous validation on a modified IEEE 69-bus distribution system and a real-world South California Edison (SCE) 47-bus network, using OPAL-RT under diverse operating conditions, underscores its robustness, model-free design capability, scalability, and adaptability to dynamic cyber graphs and system reconfiguration